How Bayes Theorem works
ฝัง
- เผยแพร่เมื่อ 31 ต.ค. 2016
- Part of the End-to-End Machine Learning School Course 191, Selected Models and Methods at e2eml.school/191
A walk through a couple of Bayesian inference examples.
The blog: brohrer.github.io/how_bayesian...
The slides: docs.google.com/presentation/...
Follow me for announcements: / _brohrer_ - วิทยาศาสตร์และเทคโนโลยี
Wow best explanation and example ever I saw ^^ Fantastic.
Excellent
exactly. These pacient disease examples were driving me nuts.
this is by far the most accessible explanation of Bayes theorem. Well done Brandon!
Best explanation of Bayes theorem I have seen. Fantastic teaching.
EXCELLENT EXPLANATION!!! I am learning graphical modeling and a lot of these concepts were a bit unclear to me. Examples given here are absolutely to the point and demystify a lot of concepts. Thank you and looking forward to more videos.
This is the most accessible explanation about Bayesian Inference. Thank you Brandon for the time taken to prepare this video. You rock !
Thank you for this excellent explanation. You are a patient and well-spoken teacher.
The way you connect things with appropriate easy to examples...Amazing...
This is the first time I've felt like I've actually understood this... and it's such a simple concept! Thank you!
Thank you sooo much Brandon for explaining the concepts so clearly.
Loved the analogies with real life philosophies, brilliant!
This is fantastic. Thank you so much! I have been exposed to BT before, but have never understood it. As sad as it sounds, I didn't realize it was composed of joint probability which is composed of conditional probability, and marginal probability. Conditional probability and joint probability, and Bayes theorem all just looked the same. This really helped clarify things for me.
Brandon I just want to tell you that you are a fantastic teacher.
Thank you very much Shirshanya. That is a huge compliment. I'm honored.
Please make more statistics videos! I have suggested your channel to my biostat teacher.
I have viewed many explanations about Bayes rule but this is no doubt the best! Thanks Brandon
Definitely the best explanation of the theorem told in an easily understandable way, I can find in the internet...
Amazing. I already knew what Bayes theorem was, but you have an awesome intro to Bayes. Thanks for the video.
when your teacher don't make sense, had to go through teaching videos online and came across this one...
Lucky lucky lucky! Thank you Mr!
Best video I found with all the information that I needed at one place.
Thanks.
0:40 Bayes wrote 2 books one about theology and one about probabilty.
He planed to write a third book infering the existence / non existence of God with probability (likelihood distribution = humanity, Prior distribution= miracles !
I have been struggling with bayesian inference and your tutorial makes it so easy to understand! Thank You! Keep up the good work.
I'm very happy to hear it Fahad. Thanks.
This video deserves more thumbs up. I understood a lot on a lazy sunday evening :) great explaination.
hands down best explanation i've seen, thank you
Great explanation and video lesson production. Best Bayesian lesson I've found on youtube
Thank you for the video, it helped me understand the concept of Bayesian inference.
The concept is simple. In a nutshell, you have an idea about what the quantity is and then you use the measurements to sharpen your assumption.
I wish everyone taught like this. Your presentation was awesome. Thank you
I must say, this is the explanation of Bayes theorem, I have ever seen..... PERFECT!!!!!
Great video!! Very nice and easy to digest explanation of Bayes theorem! Thank you very much for sharing this excellent material. I have got a better understanding on how to apply it to my problems. Keep the great work!
Thanks for the excellent video. A good refresher! Keep up the good work!
Simply the best ! Thank you Brandon
i don't know what to say, i'm a computer science student and i have never seen an explanation better than this ... thank you veryyyyy much
I was looking for intuitive content to introduce me about essence of Bayes theorem in statistics, thanks for this. Luckily I found your blog about machine learning and robotics. That's all what I wanted under a roof, robotics, data science and machine learning.
best explanation I found on the topic so far. great work!!!
Amazing videos! Thank you for making my day easier.
This was the best explanation of Bayes I've ever heard, I had such a hard time wrapping my head around it from other sources
I have been searching for explanation like this for sometime and a big WOW to this guy. Wonderful explanation!!
Excellent explanation. At the 15:20 and beyond is when everything really started to come together. Also thanks for deriving the formula at the 7:10 mark.
This is the best explanation yet, it helped me get a greater intuitive sense of Bayesian inferences.
Yes it was great. It seems running into Feigenbaum maths or simular
Bravo! Wonderful presentation. Thank you for this presentation.
Excellent explanation ! This is the manner in which mathematics must be explained. With cases of practical applicability. Good job mr. Brandon !
Thanks Razvan. I'm so happy you enjoyed it.
Thank you. Your video has been of great help. I have tried different resources to wrap my head around Bayesian theorem and always got knocked out at the front door. Excellent expalnation
Thank you so much. The way you put it is really nice and easily understandable, yet conveys the concept. :)
Excellent examples and explanation! Now everything is so much clearer. :)
the best explanation I ever seen! Super clear.
Stop looking for a descent tutorial... this one is the best!
Really I want to thank you so much
the best thing that I have ever heard about Bayesian.
you have illustrated everything and simplify the method for me
regards
Hi Brandon, your video was simple, superb, and stupendous!
Thank you for this excellent presentation!
Terrific examples and terrific explanation down to such applicable quotes!
absolutely amazing explanation
Superb lecture - esp. the MLE explanation!
I know the video is old but I have to agree with the pinned comment. I already knew Bayes Theorem buy as I don't use it often, I have to be constantly refreshing the details in my mind. TH-cam algorithm recommended this video and it's hands down the best I have ever watched.
Thank you. I really appreciate that.
the best explanation i hv seen about bayes theorem... awsome... thnx a ton....
this was very intuitive explanation, man do more!
You're very good at explaining and also you go in some details which is nice. Too often youtube tutorials are too simple. keep going.
now that you have said that (an year ago), i kinda feel like finding the probability of likelihood of a youtuber making too simple tutorials!
This helped me immensely, thank you so much!!
For 5 years i kept Bayes aside , you are the guru in teaching stuff.. God bless you Brandon
I was reading about Bayes Theory for months ! And this is the first time I understand the concept!! Wow!! such an amazing way of teaching!!
I'm so happy to hear it Taghreed. That was exactly my hope.
There are never lines at the men's room.
haha.
lol
Unless it's cocaine.
So the probability of this sample being true is 0%, hahhaha
You have never been to developer conferences :)
Thanks, very well explained, the charts are also very well prepared.
Thank you very much for the best explanation, It's very interesting
Brandon, GREAT explanations!! I am taking a "Math for Data Sciences" class and have been flying through it until the final week and "Bayes Theorem". Achk...... It was poorly explained and very confusing. I was going to drop the class as I just couldn't get it. After watching your TH-cam explanation I am excited about the possibilities and understand the way it works - cool stuff! Thank you for all you do!!!
"...one of the top 10 math tattoos of all time." 😂
hope you can do more videos!!! you are the best teacher!
Great video - this really helped a lot. Thank you so much.
Absolutely brilliant! Your presentation, examples, etc. were perfect and applicable! Thanks!
Thank you very much :)
Great explanation and simplification of a difficult concept. The three quotations at the end are poetic and purposeful. Thanks
I found them surprising relevant too. Thanks Sridhar.
The best explaination on youtube
thank you man
Lovely Explanation! Thanks a lot Brandon Rohrer
Nothing much to say only thank you! you may have helped me in clearing my exam!
such a well-thought -through video, very good explanations for every instance, the ending was the bonus, loved it, thank you
Thank you Lilit! I appreciate that.
Brandon, this was great, thank you. Very easy to follow and really interesting and concise!
Thanks Erin!
best explanation on youtube so far
i like the quotes you put at the end and how you reword them
Thank you so much for excellent explanation
Wow... amazing. You’re a great teacher!
How does the shape and mean of our prior distribution tell us the best end belief?
our prior knowledge is that the puppy weighs 14.2 lbs last time and that weight changes aren't noticeable
what if the mean of our prior distribution is chosen to be larger/smaller than 14.2 lbs?
and what if we take that distribution to be narrower/broader (more than 1 lb std)?
an extreme case, what if we first guess that the puppy's weight is *exactly* 14.2 lbs?
Just wanna say this is amazing. Thanks for sharing.
The knowledge that Bayes was a theologian and that his theory requires at least some belief or faith in improbable things earns a "Well played, Mr. Bayes" slow clap. I've been enjoying your videos Brandon, thanks for keeping things approachable!
I’m not sure that Bayesian epistemology requires a belief in improbable things. I love this video, but I think that’s an overstatement. I do think that it requires us to be open to the possibility that improbable things may be true. It does not require me to have faith in anything improbable, but rather to proportion our beliefs to the evidence (probabilities)- which is the antithesis of faith-while accepting the possibility of being wrong. To accept something as true that is improbable… is intellectually irresponsible and lacks due caution and humility. But to withhold belief (proportionally to the evidence) from improbable things is intellectually responsible and does not exclude being open to surprise-to the possibility that something improbable is true. I don’t think Bayesian epistemology intrinsically expects you to hold as true some improbable thing (faith). Abstinence from faith is acceptable in all cases as long as the possibility of error is operative. This suggestion that it’s necessary in Bayesian epistemology to believe something that is improbable was the only sloppy part of the video, no? I’m open to correction…
I think you guys got it the opposite way, the video was trying to say, be open to believe in the improbable things that come from the data (evidence), rather than only holding on a prior belief.
Great example! Very easy to follow and understand. On a side note: I showed your video to my students and some of them objected rather "emphatically".
They said it was too sexist. Crazy times we live in....Instead of math and statistics, they wanted to discuss gender roles and stereotypes in a Stat class. Gosh!
Thanks John! I agree with your students. When I watch this now, I cringe. I definitely need to re-do it with a better example, one that doesn't reinforce outdated gender norms.
@@BrandonRohrer No....Please, do not follow the mad crowds... this an innocent, simple math example. People are getting crazy and finding excuses to feel offended and start meaningless fights!
So sad. Long hair and standing in the womens restroom line and we cant even use Bayes' thereom to assume its a woman 😂
I loved the Mark Twain citation in the end! Great video thanks!
However I have one question, do you assume normal distributions of the likelihood? Why so?
Superb explanation Sir
Wow that was an amazing explanation! Thank you 😊
You're so much better than my Statistics teacher, thank you so much for this explanation!
Thanks Mathias!
thanks alot, you made it simple to understand
Excellent introduction, thanks. Is there also a continuitaton concerning a graphic prior Selektion and Jeffreys priors?
Hey @Brandon Rogers,
At 18:12, y axis is likelihood not probability. Probability is area under curve for this graph.
Excellent presentation👍
정말 감사합니다. 궁금해 왔던거 한번에 다 해결됬어요 Thank you
Amazing explanations
What a stunning explanation. Speechless
💜 🧡 🖤 💚 🤎 💛 💙
hey man,this is the one good explanation for conditional prob i had ever heard
Thanks!
great explanation!
That was fantastically done.
Thank you :)
I believe you don't know much about statistics (the impossible thing), but I do believe you really know how to explain Bayesian Inference. Great video.
You deserve many more subscribers dude
Really fantastic teaching! Thanks!
the math at 5:39 timeline has an error. it should be 0.98*4/98=0.04. i checked your blog. it was also wrong. There's no way that the product of the two numbers will become 0.04. Please correct this video and your blog
That's the best explanation ever❤️❤️❤️❤️
Great Video.
Thanks a lot!
Amazing explanation and graphics!
Thanks!
Great explanation! Thanks!