Markov Chain Monte Carlo (MCMC) : Data Science Concepts
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- เผยแพร่เมื่อ 15 มิ.ย. 2024
- Markov Chains + Monte Carlo = Really Awesome Sampling Method.
Markov Chains Video : • Markov Chains : Data S...
Monte Carlo Video : • Monte Carlo Methods : ...
Markov Chain Stationary Distribution Video : • Markov Chain Stationar...
I had two different university professors explaining MCMC, but I didn't quite get them until watching your video! Best explanation ever!
Fantastic! Note the lack of cuts and edits - this guy knows his stuff.
Hey all; here is the Markov Chain Stationary Distribution Video Link: th-cam.com/video/4sXiCxZDrTU/w-d-xo.html
Really excellent series of videos - been scratching my head over sampling methods for ages, but you explain it so succinctly and clearly it is finally making sense. Thanks for these!
Glad to help!
You have a gift for explaining things. Every question that pops into my head gets immediately answered.
Thanks!
I gotta say your videos have been super helpful for a stats subject I took last semester (which involved time series, ARIMA model, stationarity etc.) and now MCMC came out at the perfect timing. You have such a gift for explaining the intuition behind statistical concepts, and I'm looking forward to future videos from you. Your channel is a treasure!
Glad I could help!
Does anyone have a python code that uses MCMC to predict closing prices? Can I have it, thanks
I've watch a load of your videos in the last 4 or 5 days.
They are absolutely brilliant!!
I don't know what it is, but i really like this guy. Clearly knows his stuff and articulate too. Great presentation, thank you
Thank you so much, I'm a scientist myself and have used some mcmc package blindly. Now, applying what I have been doing to every step of this video made me understand the full concept super clearly.
wow!! The continuity in the explanation is just phenomenal , thanks a ton!
I'm very impressed to how clear the explanation is.
You're an awesome professor. I have finally understood MCMC and Metropolis Hastings thanks to you
One of my favorite guys. Has a great knack for knowing the right balance of intuition and rigor/formal definitions.
Great video! Much clearer than anything else I've seen or read about MCMC.
Hi Ritvik, your explanations are great in many ways. One of the best things is they are very logically coherent, leaving no gaps that require the listener to figure out. Please do keep up the splendid work. This is a major good deed for so many.
Thanks a ton!
Exactly. Was about to write the same thing!
This video has significantly improved my base understanding of MCMC, thank you so much
finally someone explained why we need markov chain. thank you!
Thanks for making this video. Finally came across the one that explain MCMC in plain words without dumping math formulas. Hope other videos and articles in follow this.
Without your video, I think I will never understand the key idea behind MCMC ! Thanks for the good work...
I have been reading a 37 pages paper without understand a thing for two hours, and you've been clear in 12 mins¡¡¡ amazing job, many thanks
The interpretation of this entire series is very helpful to understand these topics. Could you please make a video on Bayesian Regression using MCMC
You are a great presenter, it is very easy to follow you, clean logic of how you build up the reasoning step by step, I like it very much, thank you.
Your channel is so underrated, you are making absolutely sick content!
You, Sir, are a brilliant instructor...I am awed. Thank you!
Dude! That was the clearest explanation of MCMC I've ever heard. Thanks!
This is going to be super helpful for a future interview :) Thanks!
Very clear description. Thank you!
I like the way you teach. Thanks for these videos.
I have never seen such an in-depth explanation of the MCMC! Thanks a lot bro.
Do you have any python code that uses MCMC to predict closing prices? Can I have it, thanks
Great stuff. I'll be running through all your videos.
Thanks, very informative! I really like the way you explain things.
That's a very clear explanation. Thank you bro
You are great teacher! Deep respect!
That clears everything, thank you.
I found this series on MCMC really helpful for my project! Thank you for your very kind support in giving good content.
Great to hear!
a complement about why detailed balanced condition is valid if a distribution is stationary, it's because of bayesian statistics.
recall the equation P(a|b) = P(b|a)p(a)/p(b),
some rearrangement we get: p(b)P(a|b) = p(a)P(b|a)
if it's in stationary, p(a) and p(b) are const, then the equation holds, we call it detailed balanced conditon.
Awesome! Looking forward to more on McMC.
More to come!
this is an amazing explanation!
Thanks for this, really enjoyed your explination
Thank you for making this video! Your explanation is superb and easy to follow. Much appreciated!!
Thank you, this helped me a lot
Great lectures！ Awesome！
I love your videos and you really simplify concepts , my only comment is sometimes I get confused or don’t know applications for the concept
Thank you! Very helpful for me.
You're welcome!
I expect by watching this video, the percent successful uptake of this material for me is so much better than any textbook alone. YT and presenters like ritvikmath is the way to learn new STEM stuff for sure. Much faster and easier, this way. It's like when they finally translated the Bible from Latin to English, and now I'm not needing to suffer with the Latin version any more. haha
I'm speechless; your presenting style and explanatory power is insane!!! Thank you so much, I'm just getting into this stuff and the reading is tricky
Liked, subbed, etc. 👍👌😁
Shit… good stuff! I've just gone through 4 of your videos instead of going to pick up dinner. Bravo sir!
fantastic. are you just going through chris bishops book and making videos to help us out? i'm reading it atm and keep finding content on your channel. it really is quite helpful in providing intuition for a very dense subject
Hey your videos are the best!
Yin! Thanks :D
Brilliant. One word.
exceptional content!
Great video! Really liked the high-level explanation to get us comfortable with the ideas behind these methods. Quick question: I'm assuming we don't know p(x), so how do we construct a stationary distribution about p(x)?
Fantastic explanation! Now I got all the intuition I need to work through the formulas in our lecture :)
Thank you so much for this video. This is really helpful for my undergraduate research work. One thing I'm finding difficult to understand is, why do we use "thinning" in MCMC ? From what I have read so far, it aims to reduce autocorrelation - but why? Please tell me your thoughts on this problem. I appreciate it a lot. TIA
bro you litterly saving lifes hear thx
Urging for it more than for a new Netflix series!
This guy is really fantastic
Amazing !
Thank you!
Excellent pédagogue
Thanks again!
thank you so much
Clear. Thank you.
What a great video.
Very useful!
Glad you think so!
KING you are KING
it's fun to stay at the mcmc
amzing channel thanks
I wish Ian Goodfellow's book explained MCMC like you do. And I wish my professors back in university can teach and give intuition like this video. I would have been much more interested in stats and data science if it was taught properly.
you are god send!
you're a legend
Awesome
So usefull
Really great video. A quick question though, what if I want to approximate f(x)? Currently I am using a form of MCMC to do this to estimate the state probability of n samples.
Awesome thanks a tonne waiting for further videos on mcmc, could you please do a video on hamiltonian monte carlo too
Great suggestion!
Thank you, you are always the best. I am working on Bayesian network structure learning using Gibbs sampling, Could you suggest the best book or video which will help me to go through this please. Thank you.
@ritvikmath by any chance would you happen to have some notes presenting the topic in more depth? I have a general idea of the method but having trouble wrapping my head around some methods presented in papers. If not, its okay!
love the intro
So the Monte Carlo part refers to the eventual sampling from the stationary Markov Chain? I kind of missed where it comes in, except for the board title.
The Monte Carlo part refers to simulating steps through the Markov Chain. So we design a Markov Chain with some transition probabilities and then we start at some x0 and step from one state to the next which is the Monte Carlo part.
could you please make a video on Sequential monte carlo (SMC) and Hamiltonian monte carlo (HMC)
Also, could you maybe make a video on where in Data Science sampling techniques like MCMC (Gibbs, Metropolis ...) are useful? Missing data imputation? Would be highly appreciated!
How do we know the p(x) that should be the steady state of our MC? because I think the p(x) is the black box that we do not know and wants to sample from it to find it. If we have p(x), what is the obstacle against us that prevent us from sampling from it? This is a little bit confusing for me in all sampling videos on TH-cam.
How exactly should the end of the burn in be detected and decided by an iterative algorithm, when it's a random variable that is being monitored, and it is therefore jumping around (so you can't see if it goes flat compared to prior values) and you don't even have the truth value to compare with, because otherwise you'd already have your goal in hand at the very beginning?
Ehrenmann
Goated
Any chance of doing the EM algorithm?
could you please make a viideo on Sequential monte carlo SMC
I'm just here because there is a gun in Destiny 2 call Monte Carlo, which in turn has a perk called Markov Chain.
I get why it was called that now
Lol
@@ritvikmath I watched the whole video, really well done. While most of it went over my head, the concept was well explained.
Question - where does the first sample come from?
So here you say that stationary is not to have the same probability, the same number, buy to have the same p(x), which is a distribution, a function?
I get a philosophy from here. The objective is actually is to design the appropriate transition probability. It's like to build work out and healthy eating habit if you want a body goals.
Perfect analogy!
Loved your explanation but can you please organise the videos I need to see serially before watching the "Markov Chain Monte Carlo (MCMC) : Data Science Concepts" video. All the videos are scattered all over the place.
Can anyone please suggest some books that contains all of his material?
Can you do a lesson on Gaussian Copula, please?
Coool intro
When are you going to do Hamilton MCMC? Its so hard to understand.
Does anyone have a python code that uses MCMC to predict closing prices? Can I have it, thanks
One of the hypothesis of "rejection sampling" is that samples must be independent. But here there, in MCMC, they are not independent.
I can't understand why this is still acceptable.
To be clear the following comment is in no way a criticism; rather it's a line of thinking as to illuminating how I can use this tool on some project. Can you also demonstrate a powerful application or two of this powerful method, on real data from a business, institution, or science dataset? So then is this machinery intended for making better simulations? Such as...? Compared against baseline case that does not use it, how much better is the answer to the problem? Accordingly, in this vein, some excellent looking software frameworks to help use MCMC were recently very well described by Kapil Sachdeva also on TH-cam, particularly PYMC3, Stan, NumPyro, and TFProb. (Sorry for YT, but I expect YT will interfere with it if I provide a URL in this comment linking directly to Kapil Sachdeva.)
At 6:55 you say "The probability that x_B is any of these x's on this line is exactly the probability p(x)." What does this mean? It sounds like you're saying that for any number x on the line, the probability that x_B = x is p(x). But the possible values of the Markov chain form a countable set, so for any x that's not in this countable set (which is almost all points on the line) x doesn't equal any x_B. I think by "any of these x's on this line" you mean just the x values that occur in the Markov chain.