Guys, realize for a sec how cool is that we are living in the time of the Internet. I got a topic for my seminar (Monte Carlo samplings) where I need to elaborate the topic of Metropolis - Hastings sampling among others. So I started to read the book my prof recommended me, couldn't understand a sh*t so I am going to TH-cam, searching for the corresponding videos, finding this one and understand EVERYTHING. 30 years ago I would have to go to the library and ask there another book and spent there ages until I'll understand it. Now it is simple as that! Bro, thank you sooo much for the way you are explaining the stuff! Those parts with the toy examples and the intuition behind it are so helpful! This is not the first time you are saving my ass!!! From Belarus with Love!
This is an impressive alloy of math and intuition behind it - not something you get to see very often in short educational videos like this, because it's really REALLY hard to do. But you sir are one of the few exceptions. Bravo! Please never stop. I'm sorry for my English, just wanted to say how impressed I am. Have a good one!
I have tried to understand what hacks the relationship between MC and posterior probability is for the whole day; but after looking at your video, just in 20 min, I understand it. The teaching is so clear and easy to understand! Very high-quality teaching!
Hey there! Just would like to thank you for all these wonderful high-quality work you've made and shared with us. I've seen bunch of different versions of videos covering similar topics, but yours is definitely my favorite so far! Great pace control, clear explanation and wonderful teaching style. Well done man. Please keep it up! cheers! 💪👏🙏
Landed here after watching a couple of videos on M-H, and none of them were remotely as clear as your explanation! and your explanation made me really appreciate the intuitive simplicity and beauty of the math. Great work! Really wish I had a teacher like you during my bachelors :D
The first thing I do now when I don't understand a concept is to see if you have a video on it. You make the best videos on the most complicated topics and make them so easy to understand. Simply the best! Thank you for your efforts!
Came here thinking I understood Metropolis-Hastings, enriched myself with doubts during the lecture, wrapped everything up with you at the end. I'm now leaving with a more full understanding. You are an amazing teacher!
So clearly explained. After so many years, I finally understood this. Thank you so much! It would be really great if you can explain on how we can differentiate on samplings!
I watched a lot of videos for this topic and at around 15:51 thanks to your intution it flipped the switch in me and finally the reason behind all of this makes sense - feels good. Thank you so much!
In my statistics course they first presented the markov chain and then proved that its stationary distribution is the one we are looking for which was very confusing. What helped me a lot in this video is that you showed the derivation of the chain. Thanks for the great explanation and intuition at the end!
Thank you for the video. Every math is based on intuition and you give it back when I'm about to loose mine. I paused a while and put attention on the max, then I was surprised when it suddenly changed to min. LOL..
hihi thanks for the video, i paused before 10:23 and working on the intuition of this, then i realize it shoud not be max of the two, and then i drag the bar and found you secretly change max to min. But the explanation is perfect and helped a lot!!!
Look where you are currently at, look where you have been proposed to go. If the place where you have been proposed to go is of higher probability then you better go there. 👏👏❣
This is the best explanation I've came across this. I've been trying to build the intuition outside of the math. In implementing this, say through a computer simulation, I frequently see that if the acceptance probability is between 0 and 1, it's compared to a random draw of the uniform distribution. I'm missing a link in the intuition/math about this component specifically. Can you elaborate a bit more? I kind of get it, but kind of don't. Looking forward to checking out the rest of your videos!
that's actually a tricky concept to grasp; it took me some time too. Pretend the acceptance probability is 0.1. That means we want to accept this event 10% of the time and reject it 90% of the time. Now suppose we generate some uniform random number u between 0 and 1. Consider the two cases: 1) u < 0.1 : this happens with probability exactly 10% (since it came from a uniform random distribution) 2) u >= 0.1 : this happens with probability exactly 90% (since it came from a uniform random distribution) So we can exactly use the value of u to decide whether to accept or reject.
@@ritvikmath What you describe above makes that step in the implementation so much more clear! Thanks for circling back to this (and so quickly), I really appreciate it.
First of all many thanks for the nice and useful content and teaching approach. Secondly, could you introduce any textbook related your video series on Montecarlo calro, Markov chain,..., thanks in advance
Hi, very amazing explanation! Do you have an intuition behind how the Hastings factor (rg in your video) works in case of the proposal distribution is asymmetric?
hey man I love watching your videos I learn a lot from each one of them. I have noticed that I'm more likely to watch the video if the thumbnail contains you. Black background is probably not good as well. Just wanted to share it with you, maybe you should change the thumbnail format. The format of the videos themselves is really nice in my opinion, no need for change there.
A question about the unnormalized distribution f(x): In a practical situation can f(x) consist of empirical data, for example, formulated as a histogram of occurrences of some quantity?
This video is so well-made. Every time I rewatched it I learned something new. I suddenly get confused as to why we need the detailed balance condition. It seems to me that the acceptance rule works well on its own. And the acceptance rule is intuitive. Why do we need to derive the acceptance rule through the detailed balance condition?
that's to make sure you're sampling the right distribution (through the markov chain, after the burn in). The acceptance rule can be show to be sampling from that condition and to lead to the desired "markov walk" because it satisfies this condition. Also historically the algorithm was created with this in mind, I think.
Guys, realize for a sec how cool is that we are living in the time of the Internet.
I got a topic for my seminar (Monte Carlo samplings) where I need to elaborate the topic of Metropolis - Hastings sampling among others. So I started to read the book my prof recommended me, couldn't understand a sh*t so I am going to TH-cam, searching for the corresponding videos, finding this one and understand EVERYTHING. 30 years ago I would have to go to the library and ask there another book and spent there ages until I'll understand it. Now it is simple as that!
Bro, thank you sooo much for the way you are explaining the stuff! Those parts with the toy examples and the intuition behind it are so helpful!
This is not the first time you are saving my ass!!!
From Belarus with Love!
This is seriously next level teaching. I’ve never heard such a clear explanation of M-H before! Amazing job.
Thanks!
This is a topic that has a lot of layers, but you did a great job of taking it apart and putting it back together! You’re a great teacher.
Thank you so much!
Awesome. The last five minutes on intuition is especially good
This is an impressive alloy of math and intuition behind it - not something you get to see very often in short educational videos like this, because it's really REALLY hard to do. But you sir are one of the few exceptions. Bravo! Please never stop.
I'm sorry for my English, just wanted to say how impressed I am. Have a good one!
thanks for the kind words! Also, I really like how you used the word "alloy"; I'm going to start using that :)
I have tried to understand what hacks the relationship between MC and posterior probability is for the whole day; but after looking at your video, just in 20 min, I understand it. The teaching is so clear and easy to understand! Very high-quality teaching!
Hey there! Just would like to thank you for all these wonderful high-quality work you've made and shared with us. I've seen bunch of different versions of videos covering similar topics, but yours is definitely my favorite so far! Great pace control, clear explanation and wonderful teaching style. Well done man. Please keep it up! cheers! 💪👏🙏
thanks so much!!
Landed here after watching a couple of videos on M-H, and none of them were remotely as clear as your explanation! and your explanation made me really appreciate the intuitive simplicity and beauty of the math. Great work! Really wish I had a teacher like you during my bachelors :D
You did a fantastic job by explaining so many things within 20 minutes and with no jargon!
Amazing explanation! I usually do not comment on TH-cam but here I make an exception. Good job!
Your explanation of the proposal density is the best I ever found! Thank you so much for your sharing!
Thanks!
You did make the person who doesn’t have english as mother tongue understand the topic!! You have so much talent at teaching! Great job!
The first thing I do now when I don't understand a concept is to see if you have a video on it. You make the best videos on the most complicated topics and make them so easy to understand. Simply the best! Thank you for your efforts!
i agree veeray... this guy has the magic touch!
Haha absolutely! 😁
Came here thinking I understood Metropolis-Hastings, enriched myself with doubts during the lecture, wrapped everything up with you at the end. I'm now leaving with a more full understanding. You are an amazing teacher!
So clearly explained. After so many years, I finally understood this. Thank you so much! It would be really great if you can explain on how we can differentiate on samplings!
Amazing, deserves more views and could easily replace many of the lectures on MH out there!
thanks!
Very clear explaination! Specifically, I love the intuition part at the end so much. Thanks for your excellent work!
You're so welcome!
Wrapping up a statistics PhD and I still come back to this video every few months to re-calibrate my intuition
I watched a lot of videos for this topic and at around 15:51 thanks to your intution it flipped the switch in me and finally the reason behind all of this makes sense - feels good. Thank you so much!
Awesome explanation, best resource i have found to really understand the intuition behind MH. Thank for your effort!
Glad it was helpful!
You're a saint. Thanks to people like you, the world has a chance.
aw thanks!
This succeeded for me where all other videos failed.. great explanation!
You definitely deserve more exposure!! Thanks a lot for these great explanations:)
Mate, this is the most amazing and clear content re MCMC ive yet seen. incredible. thank you so much!
This is so great. Best video I have found on this topic by far.
You clarify complex concepts to make them easier to understand; this will significantly help me in my Advanced Workbook assignment, thanks.
Kindly remind, there is a typo that the MAX(1, r_{f}r_{g}) should be MIN(1, r_{f}r_{g}). Many thanks, Ritvik, your video helped me a lot.
seriously you are saving me for upcoming exams
thank you!
of course!
The series of your videos is indeed amazing! Thank you so so much!
Thanks for sharing. I think I understand MH algorithm. You are so cool to explain profound theories in simple words!
In my statistics course they first presented the markov chain and then proved that its stationary distribution is the one we are looking for which was very confusing. What helped me a lot in this video is that you showed the derivation of the chain. Thanks for the great explanation and intuition at the end!
Watched two years ago, when I was a undergrad. Now I came back watched it again and again when I am grad. Great video!
Great video and explanation. Wish those articles and videos dumping math formulas watch this video and learn now to explain.
The best explanation of Metropolis Hastings on the internet.
Dang thanks!
Your channel is super helpful. I finally understand MCMC and successfully programmed!
Great to hear!
Absolutely best math teacher on this planet. Everytime I am searching for a math concept , if there's a video by ritvikmath, I know I am saved.
Great video, the intuition part is amazing. Thanks!
Fantastic job. This is the best explanation and description of MH that I've ever heard.
Ritvikmath is the only person who was able to finally explain Bayes to me. By far the best explanation I have ever seen. A+
The explanation of ituition is great!
Very very clear summary of MH algorithm with explanation of every step. Really great and helpful work, thanks a lot !
Glad it was helpful!
Wonderful job Ritvik. Thank you.
Thanks!!
Man, this is the best presentation of Metropolis-Hastings I have seen, yet. Respect - keep up the good work!
Your explanation is next level. Thank you very much!
You're very welcome!
Thank you for the video. Every math is based on intuition and you give it back when I'm about to loose mine. I paused a while and put attention on the max, then I was surprised when it suddenly changed to min. LOL..
This is extremely helpful! Thank you so much!! Also I appreciate your sharing your own experience learning this!
Thank you so much!! This is the clearest explanation of MH I have ever seen.
best video on MH. you make a great teacher!
Truly increadible clarity, thank you very much!
Noticed your change from MAX to MIN at around 10:23. HAHAHAH, great move!
Thanks for your explanations!! Very useful and clear to help the understanding!!
Glad it was helpful!
Great job Ritvik..such a cool explanation..love it!! Keep up the good work. Cheers!!
I'm binging your videos. God tier teaching!
Great presentation and thanks for the intuition!
Incredible explanation.
You are great!!! keep going, finally, I understood the metropolis hastings algorithm idea xD
hihi thanks for the video, i paused before 10:23 and working on the intuition of this, then i realize it shoud not be max of the two, and then i drag the bar and found you secretly change max to min. But the explanation is perfect and helped a lot!!!
Amazing explanation! Thank you.
Look where you are currently at, look where you have been proposed to go. If the place where you have been proposed to go is of higher probability then you better go there. 👏👏❣
This is the best explanation I've came across this. I've been trying to build the intuition outside of the math.
In implementing this, say through a computer simulation, I frequently see that if the acceptance probability is between 0 and 1, it's compared to a random draw of the uniform distribution. I'm missing a link in the intuition/math about this component specifically. Can you elaborate a bit more? I kind of get it, but kind of don't.
Looking forward to checking out the rest of your videos!
that's actually a tricky concept to grasp; it took me some time too.
Pretend the acceptance probability is 0.1. That means we want to accept this event 10% of the time and reject it 90% of the time. Now suppose we generate some uniform random number u between 0 and 1. Consider the two cases:
1) u < 0.1 : this happens with probability exactly 10% (since it came from a uniform random distribution)
2) u >= 0.1 : this happens with probability exactly 90% (since it came from a uniform random distribution)
So we can exactly use the value of u to decide whether to accept or reject.
@@ritvikmath What you describe above makes that step in the implementation so much more clear!
Thanks for circling back to this (and so quickly), I really appreciate it.
@@ritvikmath Why not sample from a binomial distribution with p = 0.1?
man, you are so gifted as a teacher, keep up the good work :)
Love this explenation!
Wow this was such a nice explanation, kudos!
insane quality video
Yes , I was wondering why you had max instead of min at start. But you made the correction. Thanks
You're welcome!
If only I had this video 2 years before it was published.
Thank you so so much that I finally understand metropolis hasting.🎉
I'm so glad!
Excellent video! Thank you!
You are welcome!
Amazing and super helpful video! 👏🏻👏🏻
Glad it was helpful!
I'm doing a master on data science and you are saving me on bayesian stats! Thanks
I found this video very helpful after I got confused in my course. Thank you very much!
simply amazing
Very well explained! Thank you so much!
Do you have any python code that uses MCMC to predict closing prices? Can I have it, thanks
Thank you! Really amazing lesson. I really appreciate the intuition part at the end!
That is a great video, just one mistake on the whiteboard, though the idea is the same: the Max() function should be the Min() function.
I learned something! Very good video!
Amazing explanation! MH was magic to me until I watched this! Thank you 🙏
Thank you so much you explanation is the best！
Super well explained!
Unbelievable well explained! Thx!
Great video! Is Gibbs Sampling next?
Yes! Coming out later today
Crystal clear! Thank you! :)
Glad it was helpful!
This video was perfect! so clear!
and this is such an amazing method!!
First of all many thanks for the nice and useful content and teaching approach. Secondly, could you introduce any textbook related your video series on Montecarlo calro, Markov chain,..., thanks in advance
Wish there was a triple-like button. Perfect explanation. Thanks a lot!
Thank you for the intuitive explanation.
Great work!
Outstanding video
Thank you so much! That is truly helpful!
You're so welcome!
Hi, very amazing explanation! Do you have an intuition behind how the Hastings factor (rg in your video) works in case of the proposal distribution is asymmetric?
Nice video! One question: would we risk getting stuck at one of the higher-density areas when there are several peaks in p(x)
This is awesome, thank you
You're very welcome!
hey man I love watching your videos I learn a lot from each one of them. I have noticed that I'm more likely to watch the video if the thumbnail contains you. Black background is probably not good as well. Just wanted to share it with you, maybe you should change the thumbnail format. The format of the videos themselves is really nice in my opinion, no need for change there.
thank you for the feedback! I've been experimenting with different styles and direct feedback like this means so much!
it is a very amazing lecture. you are really a very good gifted teacher. pls make more videos go on educating us
A question about the unnormalized distribution f(x): In a practical situation can f(x) consist of empirical data, for example, formulated as a histogram of occurrences of some quantity?
This video is so well-made. Every time I rewatched it I learned something new. I suddenly get confused as to why we need the detailed balance condition. It seems to me that the acceptance rule works well on its own. And the acceptance rule is intuitive. Why do we need to derive the acceptance rule through the detailed balance condition?
that's to make sure you're sampling the right distribution (through the markov chain, after the burn in). The acceptance rule can be show to be sampling from that condition and to lead to the desired "markov walk" because it satisfies this condition. Also historically the algorithm was created with this in mind, I think.
Thank you, so very much for this video. It is very very helpful.
of course!
Thank you for this great explanation!
I love you man, thank you so much
this is an amzing video!