The problem with conspiracies is they keep expanding till well the whole damn planets in on it to make them work logically but the logic like a rusty bucket full of holes
It's actually surprising how many people claim to be "open minded" but then quickly retreat back to their comfort zones once presented with a radical new perspective.
It's a hard task to be open minded 24*7..sometimes it can be exhausting being too perfect or even attempting to be. I think we are mostly open minded to accept the opposing ideas when it doesn't affect us that much.
If you accept everything as being true, you are setting yourself up for bullshit. Only scientific, objective physical reality is true. Semantics, contemporary philosophy, they are bullshit mental clownery created to deceive.
Humans are built for survival, and only the truth where it is accidentally conducive to survival Confirmation bias is a cognitive bias underlying fallacious lines of reasoning. By definition, cognitive biases need to be common. Common likely because it is partly perpetuated by our biology. I imagine that believing something that everyone in the tribe does would allow them to work well together and reduce conflict? That's my guess
+Powerdriller Power The formula is useful because it is often easier to get the probabilities on the LHS of the equation. Maybe you find it "discouraging and too cryptic" because you have not read the proof, which is very short and easy. Think for a minute and you will understand it. That joke is crap and tells you very little about Baysian reasoning.
Any time you are going to give a lecture on something you should explain it first. And when I googled at the time of watching the video I didn't find anything. It so doesn't intuitively make sense after seeing the formula and watching the video. There should be a better explanation, that's all in saying.
That's because this is horseshit, and this video is the "thinking" equivalent of "BOSTON SCIENTISTS HAVE DISCOVERED A REVOLUTIONARY NEW PERFORMANCE SYSTEM. TRY THIS ONE WEIRD TRICK TO BUILD MUSCLE FAST..."
Matthew Van Helden she said that beliefs are greyscale, and that level of confidence changes as new evidence comes. She explained the relevance without giving out a formula. You were waiting for the formula and thus missed the explanation that mattered more.
Bayes' Theorem is an important tool in probability, but my statistics book was terrible in its attempt to verbalize it. Basically, it is very much as Julia described it: we think we know (with some degree of certainty) how an event will occur. However, we often learn in the future that our belief was wrong, so we go back and revise our beliefs (posterior probabilities). My book gives a very boring example having to do with credit cards and the probability that someone will default, so I won't bore anyone with those details. The implications are important, though: if something more accurate comes along than what we believe to be right and correct, we must be willing to change our paradigm in order to have a better understanding of the way the world works.
@@singhsoul1313 And what gives you the perception of being? your senses? all of which susceptible to illusions. "Cogtio ergo sum" is a good sentiment but what is perceiving you perceiving yourself?
JoeBlu That’s the point. The only knowledge that is certain is this very experience. Alan watts said “just how a knife can’t cut itself, you can’t out be being.”
Realizing most people didn't already think like this is what ended my rebellious teen phase. Learning that my parents had gifted me something from infancy most people require years of schooling to obtain.
To everyone who doesn't know, Bayes' Theorem is this: the posterior (conditional) probability P(A|B) for A, given B, is equal to the prior probability P(A) multiplied by the conditional probability P(B|A) and divided by P(B). So P(A|B) = P(B|A) * P(A) / P(B) which is big if e.g. the chance of A, irregardless of B, is large AND the chance of B, given A, is also large. Or if e.g. the prior probability of B is very small.
for those of you who don't know what baye's rule is, it's quite simple: a change in your principles because something in the world contradicts your principles. living by bayes rule means you would use this logic to your benefit. she did explain what it was in the video, however, i admit, she didn't bother stating what it was by prefacing it with "baye's rule is"
Well, part of the problem is that to go into Bayes requires more time than is alloted to this Big Think introduction video. As far as I can extrapolate, Julia only had time to talk about the benefits of using Bayes Theorem, not necessarily what it is or how to use it in everyday thinking. But about those equations: (A|B) is the probability that B is true given that A is true. That's what the symbol "|" means. I've found this out very recently. Relevance: ....hard to talk about in 500 chars
It's a very good informative video. I'm amazed by how much dislike it got. Maybe the prerequisites to understand this video is beyond those who have disliked.
Bayes' rule is where there is a higher chance of probability. too many people will cherry pick their dissent to an argument throwing out strawman and ask improbable scenarios to discredit. Bayes' rule counters those who bring up the rare of the rare that can occur saying that since 0.000001 percent chance something won't happen means it we can't judge. but in fact we can judge based on reasonable probability.
Male humans solving disagreements by display of strength via varying degrees of violence is also natural. Just because something is natural does not necessarily mean it makes sense in today's society.
I knew before looking at the comments someone would say that and I agree, but once you look it up, I bet this video is 10times more helpful than before.
She states some of the principles of Korzybski's General Semantics. Look up the book "Language in thought and action" by Hayakawa, or "Language habits in human affairs" by Irving J. Lee
She's talking about it in an ethereal sense. Those numbers are ratios. Like how much are you leaning to say yes vs how much do you want to say no. A|B = A or B, so you run the equation twice flipping values depending. Long story short, how much do you feel like going out hiking or staying in your PJs to watch netflix. Well, its cold outside, that will make PJs more appealing, and you are tired, PJs are now more appealing. So I want to go-- 30% hiking and 70% PJs, ok, PJs and netflix it is!
1:06 - open your ears people. That's when she flatout says it. It's basically "learn new stuff. see things differently". It's what we're naturally programmed to do.
She right about the example of Japanese relocation. But, Bayes Theorem does not "pwn" all conspiracy theories. In fact, in the case of the JFK assassination, Bayes rules actually upholds the case for more than one shooter.
She said at the start of the video, that she wasn't going to go into the mathematics of Baye's equation, just a way to use the concept of updating one's beliefs based on new information. She implies at least, that a lot of the time we walk around with a beliefs as though they are black and white (1 and 2). However, when we apply Baye's rule we find that our beliefs arn't as bullet proof or as infallible as we previously thought; Rather, the degree of certainty behind our beliefs is more grey scaled, as she said, (between 0%-100% - a probability if you will) meaning that given enough evidence for a belief other than our own, we can make a more rational decision. I think you also have to take into account, the video is only 3 minutes, so it would be hard to go into depth of the mathematics that are operating within Baye's equation. This is a simplified explanation for those who are laymen in mathematics.
Caleb I'm a fan of Julia's work, but even though I enjoyed the video, I agree with Will the this was not her best. She told us about some of her *conclusions* and perspective changes after *she* understood Bayes' Theorem, but she didn't explain what the theorem *is*, nor the process of using it (at least not well), nor how she reached those conclusions. She wouldn't have had to go into the mathematics, but a conceptual description would've been in order.
I don't think the idea of updating our estimate of the chance of something based on new evidence is the worst hand-wavy description of Bayes' Theorem. At least for a nontechnical audience.
P(A|B) proportional to P(A)*P(B|A) The probability that you're actually a good driver, if you've been in a car crash, is proportional to the chance that you're actually a good driver multiplied by the probability that you would get into an accident even though you're a good driver. So if you've been in a car crash, the chance that you're still actually a good driver depends greatly on the chance that even good drivers get into accidents.
What she points out in the end of the video is that we tend to stick with our beliefs very much. What you do with Bayesian reasoning, is to make sure that every piece of evidence will affect your beliefs about things, not just very large pieces of evidence. If for example there was very small signs of subterfuge amongst japanese americans, you would shift your beliefs slightly to be more suspicious. You get closer to "I'm a 100% sure they are tricking us" but might still be far away from it.
That reminded me of my religious friends. If someone is very sick and dies, God wanted him by his side. If he recovers, God saved him. Whatever happens: God did it.
Bayes' Rule basically means that the probability of something to be true is the prior probability of it being true without some piece of evidence multiplied by the probability that it's true when you do consider the new evidence. This is not disputed even by non-Bayesians. What Bayesians say is that after you've made this determination you should change your prior probability to now equal the result that you just arrived in. It's a way of iterating beliefs to be more accurate with evidence.
Okay hold your horses. Don't let Galef's video persuade you just yet of the power of what she calls “Bayes's rule”. Given Galef is clearly influential within online discussions of rationality, here are two reasons why Galef's attempt is problematic as an attempt to convince people to adopt Bayes's rule. (1) @ 0:59 - "Bayes's rule is provably the best way to think about evidence." (Galef). Bayes's Rule states: P(A|B) = {P(B|A) * P(A)} / P (B) Furthermore, P(A|B) is defined as = P(A & B)/P(B) First off, Galef doesn't acknowledge that a mere proof of a theorem is not sufficient evidence of its epistemic worth. Proofs are always relative to axioms and a theorem proved from controversial axioms is controversial too. Note that Bayes's rule presumes the truth of a formalism with the structure of the Kolmogorov axioms for probability. Writing several hundred years before Kolmogorov, and writing as a contemporary of Thomas Bayes himself, the prominent 18th-century natural philosopher Jean d'Alembert (famous for the d'Alembert wave equation in mathematics) would go on to deny as elementary aspects of the Kolmogorvian framework as the claim that P(A & B) = P(A) * P(B), for independent A and B. He argued instead that all events are conditionally dependent (Daston, 1979). So while committing oneself to the Kolmogorov axioms may prevent you from being Dutch-booked, this is merely one epistemic standard and is not necessarily “the best”. Absent an argument for this formalism's superiority over all competitors, there is no reason to agree with her that Bayes's rule is some kind of catch-all solution to problems of reasoning. (2) @ 1:13 - "And I don't think that the math behind - the math of Bayes's rule is crucial to getting benefit out of it in your own reasoning or decision making." (Galef). Considering she has a statistics degree from Columbia, this is a puzzling claim for her to make. Remember that Bayes's rule states the following: The probability of a proposition A conditional upon B (A|B) is equal to the probability of B conditional upon A (B|A) multiplied by the unconditional (sometimes known as 'priori') probability of A (A) divided by the probability unconditional probability of B. Furthermore, since P(A|B) = P(A & B)/P(B) it follows that all the probabilities used in Bayes's rule are based on unconditional (prior) probabilities. It is contentious though what is meant by the unconditional probability of an event's occurrence. After all, when I think that it is probable that the sun will rise tomorrow, I am formulating this belief based upon my probabilistic assignments of the truth about other background beliefs (.e.g - that my memory of past days is sound, that the sun somehow spotaneously cease to exist, etc.) So it seems even such basic beliefs as my belief that he sun will rise tomorrow is itself conditional argument. In fact, according to historian of probability Lorraine Daston, when Bayes was writing his 'An essay towards solving a problem in the doctrine of chances' (1763): “Section II contained the scholium that apparently caused Bayes himself doubts grave enough to withhold the essay from publication...Bayes had had misgivings about the postulate that permitted his theorem to be generalized to cases where nothing is known about the prior probability of A and B.” (Daston, 1988, p. 258). Even the great 20th-century economist John Maynard Keynes, in his influential 'Treatise on Probability' (1921), argued that all probabilistic judgments should be understood as partial entailment relationships between at least two propositions: “A great deal of confusion and error has arisen out of a failure to take due account of this relational aspect of probability...[I]f 'a' is so related to 'b', that a knowledge of it renders a probable belief in 'b' rational, we cannot conclude anything whatever about 'b' which has not reference to 'a'” (Keynes, 1921, p. - 6). While Galef is clearly a well-intentioned person, her hasty claims about Bayesianism and its history aren't helpful, and possibly harmful, for viewers trying to learn about epistemology and probability theory. Instead, she should avoid using such ideological language as “Bayes's rule is provably the best way to think about evidence." I agree with a comment below that what she is describing is simply just a certain form of common sense that isn't necessarily better than other kinds of common sense. It also doesn't follow that common sense needs to be Bayesian in method nor that Bayes's rule will help you out in your everyday life. After all, it takes a lot of time to explicitly calculate one's priors. References Daston, Lorraine. (1979). D’Alembert’s Critique of Probability Theory. Historia Mathematica. 6, pp. 259 - 279. Daston, Lorraine. (1988). Classical Probability in the Enlightenment. Princeton, NJ: Princeton University Press. Print. Keynes, John Maynard. (1921). A Treatise on Probability. London, UK: Macmillan and Co. Print.
I was expecting some actual definition of Bayes' Rule, and was mildly disappointed that no real explanation of the rule, itself, was given. However, I think part of the point of videos like these is to let people know what's out there, so they can decide what aspects of thought they're interested in, so they can go out and learn more on their own. There's lots about Bayes' theory on Google. I never would have known that if not for this video piquing my interest. So thanks! :-)
There is a flaw in this. If you are too excessive in trying to get all the facts, you will never get anything done. Sometimes you need to charge in headlong with limited information. It's a balancing act between bravery and patience. Too much patience, and nothing gets done. Too much bravery and you ruin yourself and whatever you're working on.
Nice , but what I think is that patience is more important than bravery cause the great things are made with it and not with bravery, Even if (like you say) bravery is necessary, bravery is like a surface (a shape of something:the first impact of an action) , patience is something more deep
itsiwhatitsi The mind will always come up with excuses to, "do a little more research". Jeff Bezos didn't spend 7+ years planning. He spent 7+ years *acting*.
manictiger The creator of Amazon? Is him? Ok anyway I agree with you.. what I just try to say is that patience is a value that you must have if you go true years in which not necessary happen something (like in the internet business). Also a brave man without patience , is superficial and can't really improve. What I think is that it's necessary to be brave but the patience is a very strong value that can really open your mind, in the process of improving yourself. I agree anyway: do with bravery, but think with patience
itsiwhatitsi The mind plays games on itself. Patience is important, but cowardice will mask itself as patience, if you let it. Once you conquer your own mind, then nothing can really stop you.
Explaining Bayes' theorem is extremely easy - you look at each possible outcome, you look at how likely that outcome is, and you choose the path that leads you to the outcome with highest likelihood of success (whatever success is in this situation). This being said, I too feel Julia should've at least mentioned this. The explanation, along with another 20 seconds of talking or so, would've made for a far better (and more concrete) advice, I think... =\
I watched this video because I know how to read the title, which isn't called "Julia Galef explains what Bayes' Rule is". It's a video saying where it should be applied.
As an example, say A is "I'm a scientist" and P(A) = 0.2 because about 20% of people have a job in science. Now say B is "I know Bayes' rule", then P(B|A) might be about 0.9 because most scientists (should) know it. Lastly, say 25% of people (overall) know Bayes' rule: P(B) = 0.25. Then the probability that you're a scientist, given that you know Bayes' rule, is P(A|B) = P(B|A) * P(A) / P(B) = 0.9*0.2/0.25 = 0.72, so 72%.
Aditionally: she does explain why she does not explain the complete mathematical version of the theory (because it's too complicated), but only this simplified "consequence" of the theory
I agree that three minutes and twenty three seconds for Bayes' Rule is way too little for such an important topic. Bayes' Rule captures a mathematically demonstrable and provable truth at which humans are usually intuitively bad at. Which is why we would need extra time with it.
Actually the title does not mention where Bayes rules should be applied. More it seems to give the command to "Think Rationally via Bayes' Rule". This of course implies that a complete guide on how to follow said command is forthcoming. Given both linguistic and cultural norms here in the U.S. The expectation of a description of how to think rationally (Bayes Rules) would be included in the video. I am glad you can read the title, though I am concerned with your lack of comprehension.
Translated: Opinions should be as volatile as the information that can be used to assert or question its validity. Or put into another way, conclusions should be molded (i.e. subject to change) by each new observation whether or not foreseen by accumulation of past knowledge, even if it suggests that competing, contradictory hypotheses both have similar odds.
She's trying to explain math. If she rattled off what Bayes Rule was p(A|B)=(p(B|A)*p(A))/p(B), you probably would have clicked away. Bayes rule is kind of like shaping a blob of clay into a recognizable shape. The clay is your current opinion and recognizable shape is someone else's opinion. The more info and supporting evidence for that other person's opinion is like molding the clay into the shape. But may or may not convince you in the end, since it doesn't look completely like the shape.
There is an easier way to say this: Don't hold a stake in the way you think the world works. That way when evidence comes along that is better then what you previously believed you give the new evidence the thought it deserves.
Baye's Rule or Bayesian inference is pretty basic in the introductory concept. She explained it in the first minute just fine. All it states is that the likelihood of an event is easier to predict as you gather more evidence. It seems obvious, but in practice we tend to ignore evidence in favor of our preconceived assumptions. Non-Bayesian - "If she weighs the same as a duck, then she floats, and is therefore a witch!" Bayesian - "If she weighs the same as a duck, then she is probably a duck."
A newborn sees his 1st sunset, and wonders if the sun will rise again. He assigns equal probabilities to both outcomes by placing 1 white and 1 black marble in a bag. When the sun rises, a white marble goes in the bag. The chance that a random marble from the bag will be white (belief in future sunrises) has gone from 1/2 to 2/3. The next day, the child adds 1 white marble, and the probability goes from 2/3 to 3/4. The belief is modified to become near-certainty that the sun will always rise.
So, Bayes' rule is a formalization of how to change your mind. So... formalized how? Is it a process like some sort of mental organization technique? A mathematical analog for the logical process? This video feels like the first 3 minutes of a 15 minute video. Pragmatically, what is Bayes' rule and how would one use it? These two questions are missing from the explanation. All I gleaned is that there is a thing called Bayes' rule and it would be useful to use if I ever learned how.
...part 3 what the woman in the video describes as changes in thinking when "seeped in" bayes rule for a while, is just dedicating more attention/time/resources to updating our believes, doing more of what we already do, by consciously being more focused on doing it. probably due to realizing that our current level of updating is not as good as it could be, after thinking about it (due to thinking about it after learning about the bayes rule) ...
Earl Warren somewhat had good reason to be suspicious of the Japanese after what they did to infiltrate Singapore prior to the War... Anyway so I take it Baye's rule is about being open minded.
No she did not explain "generalized Bayes rules" in specific she did not even mention them. She simply stated that they are a means of changing a belief based on new evidence. Without explanation she could be referring any process in which the input changes the output. Her statement is therefore so general as to be useless. The admissible decision rule and Bayes rules are very specific processes used in applications like decision theory.
I was about to burst a blood vessel towards the end when she STILL HADN'T TOLD US WHAT BAYES RULE IS!!! Would have appreciated a simple "here's what bayes rule is. NOW let's discuss what it means for your every day life". /sigh/
Actually , she did present a general explanation of Baye's Rule . The problem is , how much of the general public would be willing to apply that logic ? According to Michel Foucault's dismal conclusion , nobody would - we all interiorize our existence to such an extent , that no one listens to anyone else , unless it suits them .
She does explain what Bayes' rule is (though it seems to come clear only at the very last moment of the video) I think it says that when encountering new information in the world one should not simply ask "does my theory explain this", like the conspiracy theorists who always find a way to maintain their theory Instead, one should also ask "does another theory explain this better", which is a different question, that apparently most people don't ask themselves I hope she marries me for this
Problem: Bayes' rule no longer applies in QM. (although, to be fair, I should mention some people have tried to make it work) We would not benefit from making Bayes' rule an intuition, because then the whole of QM becomes even less intuitive. Also, she makes Bayes' rule sound like statistical hypothesis testing, which it isn't. But I understand what she's getting at.
Like others have commented on, I've also noticed that BigThink is going down hill - all this video needed was a short, broad explanation of what Bayes'' Rule is - it then could have been a valuable and useful video.
As she says at 1:07: "Bayes' rule is a formalization of how to change your mind when you learn new information about the world". This also happens to be the tagline of the video in the about box..
Ever meet a woman at a bar who thinks she's smarter than you and keeps dropping the same terms over and over but never really give you any information that she actually knows what the terms mean...
For those down below thinking that Bayes' theorem is just some thing you read in a statistics class or that its school level math, i'd suggest go read more on it. It gets very complicated with very abstract extensions and alternatives the more deeper you get into probability theory and combinatorics. On a side note, intelligence in women is so freakin' sexy.
Me too. Although I ascribe to the view of Edwin T. Jaynes in "Probability Theory: The Logic of Science" which refers to it as a core principle and tool but mentions that Bayes was not the first to conceive of the product rule. Also, Jaynes shows how the same evidence can lead people in different directions at the same time.
Bayesianism is great, it can be incredibly helpful in making people think more rationally and consider more alternative explanations that they might not otherwise consider. But Bayesianism does have some problems. For instance it's not clear how one would judge evidence collected in the past with it.
These are certainly great rules and I know that both of my parents, as medical professionals, learned them in their education, but that certainly isn’t always very clear in private. About the rule of thinking itself, now consider “Sherlock Homes’ rules” (“The Science of deduction) where he first gathers all important evidence and then makes a theory or conclusion.
I heard this talk a few years ago and adopted it, along with Julia's 'Scout mindset'. Then I encountered a number of Marxist concepts and became interested. Applying both Bayes and scout mindsets, Marxist theory hit nothing but the net. It comprehensively accounted for the lack of political will to solve social problems. It used new tools to make sense of complicated world systems (historical materialism and material dialectics). And then make predictions. It understands mass psychology/consiousness. Understands politics AND economics (political economy). And it's been around for over 150 years, strawmanned, hushed and twisted because it criticises the current system that is hostile to it, capitalism. It has been shocking how well it explains the human world, in comparison to all other philosophy and ideologies. Visit 'Second Thought' for simple, neat concepts.
So the chance that you think you're right, even though you're wrong, depends on the chance that you're right in the first place and the chance that you're wrong even though you think you're right. Equally, the chance that you're wrong even when you think you're right will be proportional to the chance that you might think you're right, even though you're actually wrong. It can be applied in alot of ways I guess, Bayes' rule.
And I have access to TH-cam. And I did look it up. As did everyone else who was looking for an accessible introduction to Bayes' theorem after watching this video. I found two good videos by Dr. Richard Carrier. Luckily someone else posted a comment telling me about them. If they hadn't, everyone who was interested (my comment has 85 likes) would have to seach, and find the good introductions in the results. That's why I suggested that Big Think should have posted links in the About section.
Click on random lady from big think, listen for 2 and a half minutes, look at thumbnails, click on michio kaku. That man could explain physics to a toddler.
To apply Baye’s rule, 1. Your prior belief must be realistic, and 2. The more the prior overlaps with the evidence, the outcome is more consistent. Prior belief is rather crucial than anything else in Baye’s rule. Now, if you believe that the Earth is flat a priori, then, no matter what evidence M. Kaku or N. Tyson provide you, you’ll never going to belief that the Earth is like 🌎.
That's not true, fortunately. Any possible priors will converge to the right probability distribution, provided enough evidence. It's just that, when you start with bad priors, it takes more time for the evidence you receive to push you to the right posterior. If you mean by realistic that your priors can't be of 0% or 100% exactly, then I agree. The prior is just another piece of evidence, in a way, because from prior and evidence you build a new prior that you recursively calculate with all the evidence you have amassed. If your prior for the flatness of Earth is of 80%, and other hypotheses are at lower percentages, when you'll encounter evidence that probability distribution will shift, and shift, until it matches reality.
The quote from Warren (who was state attorney-general, not governor at the time of his testimony) has been reworded to make him sound more foolish - Warren did regret his statement when he got new information!
But, if you are still curious, there seems to be a number of Bayes' Theorem/Rule introduction videos on youtube. And also that website I linked to in the other comment.
If she is referring too the Bayesian Admissible decision rule, and I think she is, then she merely describes the outcome. The rule or more to the point rules she completely omits.
The way it's phrased makes bayes rule seem like something that gives rise to conspiracy theories rather than work as a tool against such faulty reasoning... In the case of the japanese-americans for example, the fact that no sign of subterfuge amongst the japanese-americans was found, would (with bayesian resoning) update your prior belief to one that is less suspicious of japanese-americans. if however there was evidence, you would update your prior belief to one that is more suspicious.
Bayes' Rule is the equation :P And incidentally, we're using it in every day life when we do pools. For example, if you know how many men will vote for X, and how many college educated people will vote for X, you can calculate how many man who are college educated will vote for X, without doing a specific pool. Second, Math is basically a language. It's nice to know it before you talk about it. Now please continue to be rude and wrong at the same time :). Peace!
Bayes' Rule is pretty math-y, which is probably why she doesn't explain what it means. From what I've gathered though, in layman's terms Bayes' Rule is a rule used to "update" your prior beliefs of some fact/event in light of new evidence. If anyone reading these comments who knows Bayes' Rule sees this and agrees with how I put it, please like this comment so more people can see it!
last 15 seconds are it. More specifically, one could say: "you should update your confidence in your pet theory, but also every other theory you know about, and update each confidence level *in proportion to* the likelihood of your new evidence happening assuming that theory is true"
Perhaps It is explained well if you know what Bayes rule is, but the closest I got out of the video is that you should not only revise your view of the world when you find. Contradicting evi
I don't think there is a way to put it politely, but it saddens me to see people having opinions about things they do not comprehend, like statistics. I explained somewhere what is Bayes' Rule, which is a mathematical theorem, and how is it used. Mrs. Galef's video isn't only unrelated to any mathematical theorem, (those things derived from axioms, and/or other theorems, which aren't formalisations), but it's also 100% inane. I don't have any more time for this. Peace!
"I would be a hypocrite if I denied people their religious delusions." There's a difference between saying "I prefer to think about gravity as fairies pulling things together, because I find the science related to gravity distasteful" and "Yahweh tells me that homosexuals, adulterers and people working on the sabbath should be put to death". The latter delusion can cause severe harm and is not a personal matter.
This touches on one of my favourite insights into Bayesian thinking (and science in general), namely that being 100% certain about something is crippling. A probability of 1 or 0 is unassailable in the face of other evidence (at best, you have a contradiction). This has interesting implications for religious faith, or indeed any kind of dogma. I can understand the desire for certainty, but these days I actively try not to be completely certain about anything.
Perhaps that black-and-white worldview is in part what leads conspiracy theorists to hold contradictory theories as true. journals.sagepub.com/doi/abs/10.1177/1948550611434786
I’d like to think Julia did actually explain what Bayes rule is but was let down by some poor filmmaking collaborators who cut it out. Definitely needed a clear explanation.
Yes, I think so. At first, she explains the logical fallacy of this particular conspiracy theory. then she says: "Bayes' Rule is probably the best way to think about evidence. In other words, Bayes' Rule is a formalization of how to change your mind when you learn new information about the world or have new experiences." (From transcription in the video description) then she gives some examples of people who do or do not base their reasoning on Bayes' rule. But what it actually is, who knows?
Actually she explained it very well and how it affects you on a daily basis. But obviously you can't transfer that onto your own life. So you are angry on how she didn't explain it simpler.
reading a few comments here makes me feel ashamed to be human. BIG think is a brilliant channel and this lady knows her stuff, she makes good points and good examples. SOme comments here are akin to the chicken or egg argument, when you start explaining it in depth ppl simply zone out cause they dont hear the answer they want.
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The problem with conspiracies is they keep expanding till well the whole damn planets in on it to make them work logically but the logic like a rusty bucket full of holes
It's actually surprising how many people claim to be "open minded" but then quickly retreat back to their comfort zones once presented with a radical new perspective.
It's a hard task to be open minded 24*7..sometimes it can be exhausting being too perfect or even attempting to be. I think we are mostly open minded to accept the opposing ideas when it doesn't affect us that much.
Frankness, not openness is the key.
@@sanjarcode please explain your argument if you want people who encounter this reply section to take your opinion seriously, I can't be guessing
If you accept everything as being true, you are setting yourself up for bullshit. Only scientific, objective physical reality is true. Semantics, contemporary philosophy, they are bullshit mental clownery created to deceive.
Humans are built for survival, and only the truth where it is accidentally conducive to survival
Confirmation bias is a cognitive bias underlying fallacious lines of reasoning. By definition, cognitive biases need to be common. Common likely because it is partly perpetuated by our biology. I imagine that believing something that everyone in the tribe does would allow them to work well together and reduce conflict? That's my guess
You neglected to explain Bayes' rule.
+Matt Van Helden because it is so basic and a quick google gives you P(A|B) = P(A) P(B | A) / P(B)
+Powerdriller Power The formula is useful because it is often easier to get the probabilities on the LHS of the equation.
Maybe you find it "discouraging and too cryptic" because you have not read the proof, which is very short and easy. Think for a minute and you will understand it.
That joke is crap and tells you very little about Baysian reasoning.
Any time you are going to give a lecture on something you should explain it first. And when I googled at the time of watching the video I didn't find anything. It so doesn't intuitively make sense after seeing the formula and watching the video. There should be a better explanation, that's all in saying.
That's because this is horseshit, and this video is the "thinking" equivalent of "BOSTON SCIENTISTS HAVE DISCOVERED A REVOLUTIONARY NEW PERFORMANCE SYSTEM. TRY THIS ONE WEIRD TRICK TO BUILD MUSCLE FAST..."
Matthew Van Helden she said that beliefs are greyscale, and that level of confidence changes as new evidence comes. She explained the relevance without giving out a formula. You were waiting for the formula and thus missed the explanation that mattered more.
Better yet, actively try to poke holes in the things that you believe the most.
My lost bro😁
100%
That’s just called “science”. Not sure what Bayes’ rule has directly to do with it.
Bayes' Theorem is an important tool in probability, but my statistics book was terrible in its attempt to verbalize it. Basically, it is very much as Julia described it: we think we know (with some degree of certainty) how an event will occur. However, we often learn in the future that our belief was wrong, so we go back and revise our beliefs (posterior probabilities). My book gives a very boring example having to do with credit cards and the probability that someone will default, so I won't bore anyone with those details. The implications are important, though: if something more accurate comes along than what we believe to be right and correct, we must be willing to change our paradigm in order to have a better understanding of the way the world works.
The best presentation I know of can be found in "Probability Theory: The Logic of Science" by Edwin T. Jaynes.
hey are you gonna tell us what Bayes' rule is or do we have to pay for that?
Here she explains... th-cam.com/video/BrK7X_XlGB8/w-d-xo.html
LOL
@@HakWilliams Nice that a video was created to address this - eventually....
"Is there any knowledge in the world which is so certain that no reasonable man could doubt it?"
-Bertrand Russell.
Your own conscious experience.
@@singhsoul1313 What IS your conscious experience?
JoeBlu I’m being. Being is just perceiving. You see how I can perceive being.
@@singhsoul1313 And what gives you the perception of being? your senses? all of which susceptible to illusions. "Cogtio ergo sum" is a good sentiment but what is perceiving you perceiving yourself?
JoeBlu That’s the point. The only knowledge that is certain is this very experience. Alan watts said “just how a knife can’t cut itself, you can’t out be being.”
Realizing most people didn't already think like this is what ended my rebellious teen phase. Learning that my parents had gifted me something from infancy most people require years of schooling to obtain.
Duck.
Duck@@demonetizedz1001
Duck
THANKS a lot for explaining the Bayes Rule in story with no equations. Very powerful. Love from Bangladesh
To everyone who doesn't know, Bayes' Theorem is this: the posterior (conditional) probability P(A|B) for A, given B, is equal to the prior probability P(A) multiplied by the conditional probability P(B|A) and divided by P(B). So
P(A|B) = P(B|A) * P(A) / P(B)
which is big if e.g. the chance of A, irregardless of B, is large AND the chance of B, given A, is also large. Or if e.g. the prior probability of B is very small.
for those of you who don't know what baye's rule is, it's quite simple: a change in your principles because something in the world contradicts your principles. living by bayes rule means you would use this logic to your benefit. she did explain what it was in the video, however, i admit, she didn't bother stating what it was by prefacing it with "baye's rule is"
Well, part of the problem is that to go into Bayes requires more time than is alloted to this Big Think introduction video. As far as I can extrapolate, Julia only had time to talk about the benefits of using Bayes Theorem, not necessarily what it is or how to use it in everyday thinking.
But about those equations: (A|B) is the probability that B is true given that A is true. That's what the symbol "|" means. I've found this out very recently.
Relevance: ....hard to talk about in 500 chars
Thanks. My first Introduction to clear thinking was SI Hayakawa’s Language in Thought and Action years ago. Got me started thinking about thinking.
My last name is a rule! :O
you rule!
@El Pacificador are you talking about those guys who believe in reincarnation?
It's a very good informative video. I'm amazed by how much dislike it got. Maybe the prerequisites to understand this video is beyond those who have disliked.
Thanks for making sure this was a youtube comments section.
Bayes' rule is where there is a higher chance of probability.
too many people will cherry pick their dissent to an argument throwing out strawman and ask improbable scenarios to discredit.
Bayes' rule counters those who bring up the rare of the rare that can occur saying that since 0.000001 percent chance something won't happen means it we can't judge.
but in fact we can judge based on reasonable probability.
Male humans solving disagreements by display of strength via varying degrees of violence is also natural. Just because something is natural does not necessarily mean it makes sense in today's society.
I knew before looking at the comments someone would say that and I agree, but once you look it up, I bet this video is 10times more helpful than before.
She states some of the principles of Korzybski's General Semantics. Look up the book "Language in thought and action" by Hayakawa, or "Language habits in human affairs" by Irving J. Lee
She's talking about it in an ethereal sense. Those numbers are ratios. Like how much are you leaning to say yes vs how much do you want to say no.
A|B = A or B, so you run the equation twice flipping values depending.
Long story short, how much do you feel like going out hiking or staying in your PJs to watch netflix. Well, its cold outside, that will make PJs more appealing, and you are tired, PJs are now more appealing. So I want to go-- 30% hiking and 70% PJs, ok, PJs and netflix it is!
1:06 - open your ears people. That's when she flatout says it. It's basically "learn new stuff. see things differently". It's what we're naturally programmed to do.
She right about the example of Japanese relocation. But, Bayes Theorem does not "pwn" all conspiracy theories.
In fact, in the case of the JFK assassination, Bayes rules actually upholds the case for more than one shooter.
you didn't actually introduce Bayes' Rule
تا وتاواوزتظ.ططجطج
She said at the start of the video, that she wasn't going to go into the mathematics of Baye's equation, just a way to use the concept of updating one's beliefs based on new information.
She implies at least, that a lot of the time we walk around with a beliefs as though they are black and white (1 and 2). However, when we apply Baye's rule we find that our beliefs arn't as bullet proof or as infallible as we previously thought; Rather, the degree of certainty behind our beliefs is more grey scaled, as she said, (between 0%-100% - a probability if you will) meaning that given enough evidence for a belief other than our own, we can make a more rational decision.
I think you also have to take into account, the video is only 3 minutes, so it would be hard to go into depth of the mathematics that are operating within Baye's equation. This is a simplified explanation for those who are laymen in mathematics.
Caleb I'm a fan of Julia's work, but even though I enjoyed the video, I agree with Will the this was not her best.
She told us about some of her *conclusions* and perspective changes after *she* understood Bayes' Theorem, but she didn't explain what the theorem *is*, nor the process of using it (at least not well), nor how she reached those conclusions. She wouldn't have had to go into the mathematics, but a conceptual description would've been in order.
I don't think the idea of updating our estimate of the chance of something based on new evidence is the worst hand-wavy description of Bayes' Theorem. At least for a nontechnical audience.
Here she explains: th-cam.com/video/BrK7X_XlGB8/w-d-xo.html
P(A|B) proportional to P(A)*P(B|A)
The probability that you're actually a good driver, if you've been in a car crash, is proportional to the chance that you're actually a good driver multiplied by the probability that you would get into an accident even though you're a good driver.
So if you've been in a car crash, the chance that you're still actually a good driver depends greatly on the chance that even good drivers get into accidents.
would be nice to explain what they bayes' rule actually is.
What she points out in the end of the video is that we tend to stick with our beliefs very much. What you do with Bayesian reasoning, is to make sure that every piece of evidence will affect your beliefs about things, not just very large pieces of evidence.
If for example there was very small signs of subterfuge amongst japanese americans, you would shift your beliefs slightly to be more suspicious. You get closer to "I'm a 100% sure they are tricking us" but might still be far away from it.
That reminded me of my religious friends. If someone is very sick and dies, God wanted him by his side. If he recovers, God saved him. Whatever happens: God did it.
If I achieve something, my mom would thank God. If something bad happened, it was either on me or bad luck. This frustrated me to no end.
Bayes' Rule basically means that the probability of something to be true is the prior probability of it being true without some piece of evidence multiplied by the probability that it's true when you do consider the new evidence.
This is not disputed even by non-Bayesians. What Bayesians say is that after you've made this determination you should change your prior probability to now equal the result that you just arrived in. It's a way of iterating beliefs to be more accurate with evidence.
Okay hold your horses. Don't let Galef's video persuade you just yet of the power of what she calls “Bayes's rule”. Given Galef is clearly influential within online discussions of rationality, here are two reasons why Galef's attempt is problematic as an attempt to convince people to adopt Bayes's rule.
(1) @ 0:59 - "Bayes's rule is provably the best way to think about evidence." (Galef).
Bayes's Rule states: P(A|B) = {P(B|A) * P(A)} / P (B)
Furthermore, P(A|B) is defined as = P(A & B)/P(B)
First off, Galef doesn't acknowledge that a mere proof of a theorem is not sufficient evidence of its epistemic worth. Proofs are always relative to axioms and a theorem proved from controversial axioms is controversial too. Note that Bayes's rule presumes the truth of a formalism with the structure of the Kolmogorov axioms for probability. Writing several hundred years before Kolmogorov, and writing as a contemporary of Thomas Bayes himself, the prominent 18th-century natural philosopher Jean d'Alembert (famous for the d'Alembert wave equation in mathematics) would go on to deny as elementary aspects of the Kolmogorvian framework as the claim that P(A & B) = P(A) * P(B), for independent A and B. He argued instead that all events are conditionally dependent (Daston, 1979). So while committing oneself to the Kolmogorov axioms may prevent you from being Dutch-booked, this is merely one epistemic standard and is not necessarily “the best”. Absent an argument for this formalism's superiority over all competitors, there is no reason to agree with her that Bayes's rule is some kind of catch-all solution to problems of reasoning.
(2) @ 1:13 - "And I don't think that the math behind - the math of Bayes's rule is crucial to getting benefit out of it in your own reasoning or decision making." (Galef).
Considering she has a statistics degree from Columbia, this is a puzzling claim for her to make. Remember that Bayes's rule states the following: The probability of a proposition A conditional upon B (A|B) is equal to the probability of B conditional upon A (B|A) multiplied by the unconditional (sometimes known as 'priori') probability of A (A) divided by the probability unconditional probability of B. Furthermore, since P(A|B) = P(A & B)/P(B) it follows that all the probabilities used in Bayes's rule are based on unconditional (prior) probabilities.
It is contentious though what is meant by the unconditional probability of an event's occurrence. After all, when I think that it is probable that the sun will rise tomorrow, I am formulating this belief based upon my probabilistic assignments of the truth about other background beliefs (.e.g - that my memory of past days is sound, that the sun somehow spotaneously cease to exist, etc.) So it seems even such basic beliefs as my belief that he sun will rise tomorrow is itself conditional argument.
In fact, according to historian of probability Lorraine Daston, when Bayes was writing his 'An essay towards solving a problem in the doctrine of chances' (1763): “Section II contained the scholium that apparently caused Bayes himself doubts grave enough to withhold the essay from publication...Bayes had had misgivings about the postulate that permitted his theorem to be generalized to cases where nothing is known about the prior probability of A and B.” (Daston, 1988, p. 258).
Even the great 20th-century economist John Maynard Keynes, in his influential 'Treatise on Probability' (1921), argued that all probabilistic judgments should be understood as partial entailment relationships between at least two propositions: “A great deal of confusion and error has arisen out of a failure to take due account of this relational aspect of probability...[I]f 'a' is so related to 'b', that a knowledge of it renders a probable belief in 'b' rational, we cannot conclude anything whatever about 'b' which has not reference to 'a'” (Keynes, 1921, p. - 6).
While Galef is clearly a well-intentioned person, her hasty claims about Bayesianism and its history aren't helpful, and possibly harmful, for viewers trying to learn about epistemology and probability theory. Instead, she should avoid using such ideological language as “Bayes's rule is provably the best way to think about evidence." I agree with a comment below that what she is describing is simply just a certain form of common sense that isn't necessarily better than other kinds of common sense. It also doesn't follow that common sense needs to be Bayesian in method nor that Bayes's rule will help you out in your everyday life. After all, it takes a lot of time to explicitly calculate one's priors.
References
Daston, Lorraine. (1979). D’Alembert’s Critique of Probability Theory. Historia
Mathematica. 6, pp. 259 - 279.
Daston, Lorraine. (1988). Classical Probability in the Enlightenment. Princeton, NJ: Princeton University Press. Print.
Keynes, John Maynard. (1921). A Treatise on Probability. London, UK: Macmillan and Co. Print.
Best discussion on this matter on this thread. Thanks
You're what I call Crippled Smart.
I was expecting some actual definition of Bayes' Rule, and was mildly disappointed that no real explanation of the rule, itself, was given. However, I think part of the point of videos like these is to let people know what's out there, so they can decide what aspects of thought they're interested in, so they can go out and learn more on their own. There's lots about Bayes' theory on Google. I never would have known that if not for this video piquing my interest. So thanks! :-)
There is a flaw in this. If you are too excessive in trying to get all the facts, you will never get anything done. Sometimes you need to charge in headlong with limited information.
It's a balancing act between bravery and patience. Too much patience, and nothing gets done. Too much bravery and you ruin yourself and whatever you're working on.
Nice , but what I think is that patience is more important than bravery cause the great things are made with it and not with bravery, Even if (like you say) bravery is necessary, bravery is like a surface (a shape of something:the first impact of an action) , patience is something more deep
itsiwhatitsi
The mind will always come up with excuses to, "do a little more research".
Jeff Bezos didn't spend 7+ years planning.
He spent 7+ years *acting*.
manictiger The creator of Amazon? Is him? Ok anyway I agree with you.. what I just try to say is that patience is a value that you must have if you go true years in which not necessary happen something (like in the internet business). Also a brave man without patience , is superficial and can't really improve. What I think is that it's necessary to be brave but the patience is a very strong value that can really open your mind, in the process of improving yourself. I agree anyway: do with bravery, but think with patience
itsiwhatitsi
The mind plays games on itself. Patience is important, but cowardice will mask itself as patience, if you let it.
Once you conquer your own mind, then nothing can really stop you.
manictiger That's interesting
Explaining Bayes' theorem is extremely easy - you look at each possible outcome, you look at how likely that outcome is, and you choose the path that leads you to the outcome with highest likelihood of success (whatever success is in this situation).
This being said, I too feel Julia should've at least mentioned this. The explanation, along with another 20 seconds of talking or so, would've made for a far better (and more concrete) advice, I think... =\
Love this: the use of mathematical principles outside mathematics :) awesome
Yes!
I watched this video because I know how to read the title, which isn't called "Julia Galef explains what Bayes' Rule is". It's a video saying where it should be applied.
As an example, say A is "I'm a scientist" and P(A) = 0.2 because about 20% of people have a job in science. Now say B is "I know Bayes' rule", then P(B|A) might be about 0.9 because most scientists (should) know it. Lastly, say 25% of people (overall) know Bayes' rule: P(B) = 0.25. Then the probability that you're a scientist, given that you know Bayes' rule, is P(A|B) = P(B|A) * P(A) / P(B) = 0.9*0.2/0.25 = 0.72, so 72%.
Aditionally: she does explain why she does not explain the complete mathematical version of the theory (because it's too complicated), but only this simplified "consequence" of the theory
I agree that three minutes and twenty three seconds for Bayes' Rule is way too little for such an important topic. Bayes' Rule captures a mathematically demonstrable and provable truth at which humans are usually intuitively bad at. Which is why we would need extra time with it.
Actually the title does not mention where Bayes rules should be applied. More it seems to give the command to "Think Rationally via Bayes' Rule". This of course implies that a complete guide on how to follow said command is forthcoming. Given both linguistic and cultural norms here in the U.S. The expectation of a description of how to think rationally (Bayes Rules) would be included in the video. I am glad you can read the title, though I am concerned with your lack of comprehension.
Translated: Opinions should be as volatile as the information that can be used to assert or question its validity. Or put into another way, conclusions should be molded (i.e. subject to change) by each new observation whether or not foreseen by accumulation of past knowledge, even if it suggests that competing, contradictory hypotheses both have similar odds.
She's trying to explain math. If she rattled off what Bayes Rule was p(A|B)=(p(B|A)*p(A))/p(B), you probably would have clicked away.
Bayes rule is kind of like shaping a blob of clay into a recognizable shape.
The clay is your current opinion and recognizable shape is someone else's opinion. The more info and supporting evidence for that other person's opinion is like molding the clay into the shape. But may or may not convince you in the end, since it doesn't look completely like the shape.
A video about Bayes' Rule that doesn't say what Bayes' Rule is 🤔
There is an easier way to say this: Don't hold a stake in the way you think the world works. That way when evidence comes along that is better then what you previously believed you give the new evidence the thought it deserves.
if you don't stand on any side of the road without any degree of commitment, you'll be hit by the crossfire
Baye's Rule or Bayesian inference is pretty basic in the introductory concept. She explained it in the first minute just fine. All it states is that the likelihood of an event is easier to predict as you gather more evidence. It seems obvious, but in practice we tend to ignore evidence in favor of our preconceived assumptions.
Non-Bayesian - "If she weighs the same as a duck, then she floats, and is therefore a witch!"
Bayesian - "If she weighs the same as a duck, then she is probably a duck."
A newborn sees his 1st sunset, and wonders if the sun will rise again. He assigns equal probabilities to both outcomes by placing 1 white and 1 black marble in a bag. When the sun rises, a white marble goes in the bag. The chance that a random marble from the bag will be white (belief in future sunrises) has gone from 1/2 to 2/3. The next day, the child adds 1 white marble, and the probability goes from 2/3 to 3/4. The belief is modified to become near-certainty that the sun will always rise.
So, Bayes' rule is a formalization of how to change your mind. So... formalized how? Is it a process like some sort of mental organization technique? A mathematical analog for the logical process? This video feels like the first 3 minutes of a 15 minute video. Pragmatically, what is Bayes' rule and how would one use it? These two questions are missing from the explanation. All I gleaned is that there is a thing called Bayes' rule and it would be useful to use if I ever learned how.
...part 3
what the woman in the video describes as changes in thinking when "seeped in" bayes rule for a while, is just dedicating more attention/time/resources to updating our believes, doing more of what we already do, by consciously being more focused on doing it. probably due to realizing that our current level of updating is not as good as it could be, after thinking about it (due to thinking about it after learning about the bayes rule)
...
Nice that the majority here have commented on Bayes' rule rather than Julia's natural good looks.
Nice to see Julia on big think.
Earl Warren somewhat had good reason to be suspicious of the Japanese after what they did to infiltrate Singapore prior to the War...
Anyway so I take it Baye's rule is about being open minded.
No she did not explain "generalized Bayes rules" in specific she did not even mention them. She simply stated that they are a means of changing a belief based on new evidence. Without explanation she could be referring any process in which the input changes the output. Her statement is therefore so general as to be useless. The admissible decision rule and Bayes rules are very specific processes used in applications like decision theory.
I was about to burst a blood vessel towards the end when she STILL HADN'T TOLD US WHAT BAYES RULE IS!!! Would have appreciated a simple "here's what bayes rule is. NOW let's discuss what it means for your every day life". /sigh/
Actually , she did present a general explanation of Baye's Rule . The problem
is , how much of the general public would be willing to apply that logic ?
According to Michel Foucault's dismal conclusion , nobody would - we all
interiorize our existence to such an extent , that no one listens to
anyone else , unless it suits them .
She does explain what Bayes' rule is (though it seems to come clear only at the very last moment of the video)
I think it says that when encountering new information in the world one should not simply ask "does my theory explain this", like the conspiracy theorists who always find a way to maintain their theory
Instead, one should also ask "does another theory explain this better", which is a different question, that apparently most people don't ask themselves
I hope she marries me for this
Problem: Bayes' rule no longer applies in QM. (although, to be fair, I should mention some people have tried to make it work) We would not benefit from making Bayes' rule an intuition, because then the whole of QM becomes even less intuitive. Also, she makes Bayes' rule sound like statistical hypothesis testing, which it isn't. But I understand what she's getting at.
I know right, they talk about it but refuse to give us a way to learn more about it.
Like others have commented on, I've also noticed that BigThink is going down hill - all this video needed was a short, broad explanation of what Bayes'' Rule is - it then could have been a valuable and useful video.
As she says at 1:07: "Bayes' rule is a formalization of how to change your mind when you learn new information about the world". This also happens to be the tagline of the video in the about box..
Ever meet a woman at a bar who thinks she's smarter than you and keeps dropping the same terms over and over but never really give you any information that she actually knows what the terms mean...
For those down below thinking that Bayes' theorem is just some thing you read in a statistics class or that its school level math, i'd suggest go read more on it. It gets very complicated with very abstract extensions and alternatives the more deeper you get into probability theory and combinatorics.
On a side note, intelligence in women is so freakin' sexy.
+DaSnarky Remarky
it is but this woman has demonstrated absolutely no intelligence wjatsoever in this video
I would've loved to hear about the fundamentals of the rule itself. What does it stipulate?
Me too. Although I ascribe to the view of Edwin T. Jaynes in "Probability Theory: The Logic of Science" which refers to it as a core principle and tool but mentions that Bayes was not the first to conceive of the product rule. Also, Jaynes shows how the same evidence can lead people in different directions at the same time.
Bayesianism is great, it can be incredibly helpful in making people think more rationally and consider more alternative explanations that they might not otherwise consider.
But Bayesianism does have some problems. For instance it's not clear how one would judge evidence collected in the past with it.
These are certainly great rules and I know that both of my parents, as medical professionals, learned them in their education, but that certainly isn’t always very clear in private. About the rule of thinking itself, now consider “Sherlock Homes’ rules” (“The Science of deduction) where he first gathers all important evidence and then makes a theory or conclusion.
I heard this talk a few years ago and adopted it, along with Julia's 'Scout mindset'.
Then I encountered a number of Marxist concepts and became interested. Applying both Bayes and scout mindsets, Marxist theory hit nothing but the net. It comprehensively accounted for the lack of political will to solve social problems. It used new tools to make sense of complicated world systems (historical materialism and material dialectics). And then make predictions.
It understands mass psychology/consiousness. Understands politics AND economics (political economy).
And it's been around for over 150 years, strawmanned, hushed and twisted because it criticises the current system that is hostile to it, capitalism.
It has been shocking how well it explains the human world, in comparison to all other philosophy and ideologies.
Visit 'Second Thought' for simple, neat concepts.
So the chance that you think you're right, even though you're wrong, depends on the chance that you're right in the first place and the chance that you're wrong even though you think you're right.
Equally, the chance that you're wrong even when you think you're right will be proportional to the chance that you might think you're right, even though you're actually wrong.
It can be applied in alot of ways I guess, Bayes' rule.
And I have access to TH-cam. And I did look it up. As did everyone else who was looking for an accessible introduction to Bayes' theorem after watching this video.
I found two good videos by Dr. Richard Carrier. Luckily someone else posted a comment telling me about them. If they hadn't, everyone who was interested (my comment has 85 likes) would have to seach, and find the good introductions in the results.
That's why I suggested that Big Think should have posted links in the About section.
Click on random lady from big think, listen for 2 and a half minutes, look at thumbnails, click on michio kaku. That man could explain physics to a toddler.
To apply Baye’s rule, 1. Your prior belief must be realistic, and 2. The more the prior overlaps with the evidence, the outcome is more consistent. Prior belief is rather crucial than anything else in Baye’s rule. Now, if you believe that the Earth is flat a priori, then, no matter what evidence M. Kaku or N. Tyson provide you, you’ll never going to belief that the Earth is like 🌎.
That's not true, fortunately. Any possible priors will converge to the right probability distribution, provided enough evidence. It's just that, when you start with bad priors, it takes more time for the evidence you receive to push you to the right posterior. If you mean by realistic that your priors can't be of 0% or 100% exactly, then I agree.
The prior is just another piece of evidence, in a way, because from prior and evidence you build a new prior that you recursively calculate with all the evidence you have amassed. If your prior for the flatness of Earth is of 80%, and other hypotheses are at lower percentages, when you'll encounter evidence that probability distribution will shift, and shift, until it matches reality.
She explained the rule just fine...
Suggestion for all: Skip to 3:02
The rest doesn't matter.
The quote from Warren (who was state attorney-general, not governor at the time of his testimony) has been reworded to make him sound more foolish - Warren did regret his statement when he got new information!
But, if you are still curious, there seems to be a number of Bayes' Theorem/Rule introduction videos on youtube.
And also that website I linked to in the other comment.
If she is referring too the Bayesian Admissible decision rule, and I think she is, then she merely describes the outcome. The rule or more to the point rules she completely omits.
Would have been nice if she had talked about what the rule is as well
The way it's phrased makes bayes rule seem like something that gives rise to conspiracy theories rather than work as a tool against such faulty reasoning...
In the case of the japanese-americans for example, the fact that no sign of subterfuge amongst the japanese-americans was found, would (with bayesian resoning) update your prior belief to one that is less suspicious of japanese-americans. if however there was evidence, you would update your prior belief to one that is more suspicious.
So instead of watching the video, basically Bayes Rule = think of counter arguments to your believed solution
Bayes' Rule is the equation :P And incidentally, we're using it in every day life when we do pools. For example, if you know how many men will vote for X, and how many college educated people will vote for X, you can calculate how many man who are college educated will vote for X, without doing a specific pool. Second, Math is basically a language. It's nice to know it before you talk about it. Now please continue to be rude and wrong at the same time :). Peace!
She is saying: "Just ask yourself if there is a better or different explanation of everything around you then the explanation you have.
I think it would be appropriate for Big Think to provide some links to good introductory articles on Bayes' rule in the About section of this video.
Bayes' Rule is pretty math-y, which is probably why she doesn't explain what it means. From what I've gathered though, in layman's terms Bayes' Rule is a rule used to "update" your prior beliefs of some fact/event in light of new evidence.
If anyone reading these comments who knows Bayes' Rule sees this and agrees with how I put it, please like this comment so more people can see it!
last 15 seconds are it. More specifically, one could say:
"you should update your confidence in your pet theory, but also every other theory you know about, and update each confidence level *in proportion to* the likelihood of your new evidence happening assuming that theory is true"
There's a decent explanation of Bayes' Rule on Wikipedia.
which is why i changed my view a couple years ago. i should have said "i used to think" rather then "thought"
Dr. Richard Carrier gives a talk which can be found on TH-cam explaining Bayes Theorem in depth.
Perhaps It is explained well if you know what Bayes rule is, but the closest I got out of the video is that you should not only revise your view of the world when you find. Contradicting evi
I don't think there is a way to put it politely, but it saddens me to see people having opinions about things they do not comprehend, like statistics. I explained somewhere what is Bayes' Rule, which is a mathematical theorem, and how is it used. Mrs. Galef's video isn't only unrelated to any mathematical theorem, (those things derived from axioms, and/or other theorems, which aren't formalisations), but it's also 100% inane. I don't have any more time for this. Peace!
"I would be a hypocrite if I denied people their religious delusions."
There's a difference between saying "I prefer to think about gravity as fairies pulling things together, because I find the science related to gravity distasteful" and "Yahweh tells me that homosexuals, adulterers and people working on the sabbath should be put to death". The latter delusion can cause severe harm and is not a personal matter.
This touches on one of my favourite insights into Bayesian thinking (and science in general), namely that being 100% certain about something is crippling. A probability of 1 or 0 is unassailable in the face of other evidence (at best, you have a contradiction).
This has interesting implications for religious faith, or indeed any kind of dogma. I can understand the desire for certainty, but these days I actively try not to be completely certain about anything.
Perhaps that black-and-white worldview is in part what leads conspiracy theorists to hold contradictory theories as true.
journals.sagepub.com/doi/abs/10.1177/1948550611434786
I’d like to think Julia did actually explain what Bayes rule is but was let down by some poor filmmaking collaborators who cut it out. Definitely needed a clear explanation.
Never understood why FDR is put at the top of great presidents when he had this under his watch.
Would be nice to actually include the rule in this video. It only would have taken a few seconds.
From the first sentence she explained what Bayes Rule is.
Actually you showed and proved in what you wrote Bayes Rule... which is ironic.
Yes, I think so.
At first, she explains the logical fallacy of this particular conspiracy theory.
then she says:
"Bayes' Rule is probably the best way to think about evidence. In other words, Bayes' Rule is a formalization of how to change your mind when you learn new information about the world or have new experiences." (From transcription in the video description)
then she gives some examples of people who do or do not base their reasoning on Bayes' rule. But what it actually is, who knows?
It's refreshing to hear someone who doesn't need to "upspeak" every other sentence.
Actually she explained it very well and how it affects you on a daily basis. But obviously you can't transfer that onto your own life. So you are angry on how she didn't explain it simpler.
reading a few comments here makes me feel ashamed to be human. BIG think is a brilliant channel and this lady knows her stuff, she makes good points and good examples. SOme comments here are akin to the chicken or egg argument, when you start explaining it in depth ppl simply zone out cause they dont hear the answer they want.
yes, you missed her explaining why she brought it up.
The fact that she didn't explain Bayes' rule would be an issue if she wasn't hot.
In short, she's talking about sophism.it's college stuff we learn in Philo class here
This guy just asked "Why are women insecure ?", I ask "aren't we all ?"