My dude, I don't often need your teachings, but when I do you are able to single-handedly overshadow most of my past professors. I've watched in the past 4 years a good chunk of your videos and you didn't do a single one in which I didn't add some new view, even if small, on the topic. Keep it up with the work.
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!
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.
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.
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!
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.
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.
REQUEST: Please organize this playlist in sequential / logical order. Example: The first video of this playlist is Markov Chains (MCMC) which refers to a previous video for accept-reject sampling; but that video is 13th in this playlist. So it's like watching random stuff here.
At the very end it took me a second watch to realize that of course the sum of all probabilities for x given y would be 1 and thus you would get p(y) on the right hand (so obvious when you type it out :') ). Once again a great video. I think you really hit a sweet spot where people with basic math skills, can benefit from your succinct yet in depth explanations.
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
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
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
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. 👍👌😁
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.
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 RUclips, 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.)
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.
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)?
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.
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!
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.
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.
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.
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 RUclips.
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.
@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!
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?
I had two different university professors explaining MCMC, but I didn't quite get them until watching your video! Best explanation ever!
My dude, I don't often need your teachings, but when I do you are able to single-handedly overshadow most of my past professors.
I've watched in the past 4 years a good chunk of your videos and you didn't do a single one in which I didn't add some new view, even if small, on the topic.
Keep it up with the work.
Fantastic! Note the lack of cuts and edits - this guy knows his stuff.
I don't know what it is, but i really like this guy. Clearly knows his stuff and articulate too. Great presentation, thank you
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
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.
You have a gift for explaining things. Every question that pops into my head gets immediately answered.
Thanks!
I'm very impressed to how clear the explanation is.
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!
finally someone explained why we need markov chain. thank you!
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!
wow!! The continuity in the explanation is just phenomenal , thanks a ton!
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.
Your channel is so underrated, you are making absolutely sick content!
The interpretation of this entire series is very helpful to understand these topics. Could you please make a video on Bayesian Regression using MCMC
Without your video, I think I will never understand the key idea behind MCMC ! Thanks for the good work...
One of my favorite guys. Has a great knack for knowing the right balance of intuition and rigor/formal definitions.
This video has significantly improved my base understanding of MCMC, thank you so much
You're an awesome professor. I have finally understood MCMC and Metropolis Hastings thanks to you
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
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!
I've watch a load of your videos in the last 4 or 5 days.
They are absolutely brilliant!!
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.
REQUEST: Please organize this playlist in sequential / logical order. Example: The first video of this playlist is Markov Chains (MCMC) which refers to a previous video for accept-reject sampling; but that video is 13th in this playlist. So it's like watching random stuff here.
Dude! That was the clearest explanation of MCMC I've ever heard. Thanks!
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.
Great video! Much clearer than anything else I've seen or read about MCMC.
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
You, Sir, are a brilliant instructor...I am awed. Thank you!
At the very end it took me a second watch to realize that of course the sum of all probabilities for x given y would be 1 and thus you would get p(y) on the right hand (so obvious when you type it out :') ). Once again a great video. I think you really hit a sweet spot where people with basic math skills, can benefit from your succinct yet in depth explanations.
This is going to be super helpful for a future interview :) Thanks!
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
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
Awesome! Looking forward to more on McMC.
More to come!
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
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
Thanks for sharing. I begin to love learning.
Great stuff. I'll be running through all your videos.
Urging for it more than for a new Netflix series!
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. 👍👌😁
This video just save my day
You're so welcome!
I like the way you teach. Thanks for these videos.
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.
Fantastic explanation! Now I got all the intuition I need to work through the formulas in our lecture :)
Thank you for making this video! Your explanation is superb and easy to follow. Much appreciated!!
That clears everything, thank you.
You are great teacher! Deep respect!
That's a very clear explanation. Thank you bro
Very clear description. Thank you!
Thanks, very informative! I really like the way you explain things.
This guy is really fantastic
Shit… good stuff! I've just gone through 4 of your videos instead of going to pick up dinner. Bravo sir!
Brilliant. One word.
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 RUclips, 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.)
bro you litterly saving lifes hear thx
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.
Hey all; here is the Markov Chain Stationary Distribution Video Link: ruclips.net/video/4sXiCxZDrTU/видео.html
this is an amazing explanation!
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)?
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.
Hey your videos are the best!
Yin! Thanks :D
I like your concepts. Do you have any reference (books) for citation, if I want to add your formulae in my presentation for reference.
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!
Thanks for this, really enjoyed your explination
Awesome thanks a tonne waiting for further videos on mcmc, could you please do a video on hamiltonian monte carlo too
Great suggestion!
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.
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.
exceptional content!
Amazing !
KING you are KING
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!
Great lectures！ Awesome！
you are god send!
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 RUclips.
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.
@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!
you're a legend
Excellent pédagogue
Thank you, this helped me a lot
it's fun to stay at the mcmc
What a great video.
Thank you! Very helpful for me.
You're welcome!
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?
love the intro
Can you do a lesson on Gaussian Copula, please?
Clear. Thank you.
could you please make a video on Sequential monte carlo (SMC) and Hamiltonian monte carlo (HMC)
Thanks again!
Very useful!
Glad you think so!
When are you going to do Hamilton MCMC? Its so hard to understand.
the hat is dope
Thank you!
Any chance of doing the EM algorithm?
Goated
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?
could you please make a viideo on Sequential monte carlo SMC
Question - where does the first sample come from?
amzing channel thanks
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.
Why not use the full history of previous samoles instead of the immediate previous sample?