Metropolis - Hastings : Data Science Concepts

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  • Опубликовано: 24 дек 2024

Комментарии • 224

  • @dzmitrykoniukhau1362
    @dzmitrykoniukhau1362 3 года назад +114

    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 RUclips, 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!

  • @ericpenarium
    @ericpenarium 3 года назад +95

    This is seriously next level teaching. I’ve never heard such a clear explanation of M-H before! Amazing job.

  • @michaelzumpano7318
    @michaelzumpano7318 3 года назад +128

    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.

  • @shutonggu5478
    @shutonggu5478 11 месяцев назад +4

    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!

  • @zypresse2726
    @zypresse2726 Месяц назад +2

    wow I didn't notice how 18 minutes passed by... well done! thanks so much !

  • @edwardmartin100
    @edwardmartin100 3 года назад +19

    Awesome. The last five minutes on intuition is especially good

  • @MrTSkV
    @MrTSkV 3 года назад +62

    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!

    • @ritvikmath
      @ritvikmath  3 года назад +11

      thanks for the kind words! Also, I really like how you used the word "alloy"; I'm going to start using that :)

  • @zmsalex232
    @zmsalex232 23 дня назад

    You are awesome. I watched from Inverse Transformation Method, Rejection Method to this Metropolis-Hasting Method. I was previously confused about those concepts taught by my university lecturer but now I fully understands them all. Thank you so much for your wonderful and insightful teaching video.

  • @vickylim3213
    @vickylim3213 Год назад

    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!

  • @jmaes678
    @jmaes678 19 дней назад

    You explain this incredibly well! I cant put into words how helpful your videos have been

  • @filippobargagna
    @filippobargagna Год назад

    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!

  • @rishi71095
    @rishi71095 Год назад +1

    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!

    • @Yesuuh
      @Yesuuh Год назад +1

      i agree veeray... this guy has the magic touch!

    • @rishi71095
      @rishi71095 Год назад +1

      Haha absolutely! 😁

  • @skua-se1bp
    @skua-se1bp 2 года назад +2

    You did a fantastic job by explaining so many things within 20 minutes and with no jargon!

  • @PhilippeZwick
    @PhilippeZwick 11 месяцев назад

    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!

  • @JayleeWu
    @JayleeWu Год назад +3

    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! 💪👏🙏

  • @Anzah1
    @Anzah1 3 года назад +3

    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

  • @aarontan5748
    @aarontan5748 Год назад

    Watched two years ago, when I was a undergrad. Now I came back watched it again and again when I am grad. Great video!

  • @MegaNightdude
    @MegaNightdude 3 года назад +1

    You're a saint. Thanks to people like you, the world has a chance.

  • @NuclearSpinach
    @NuclearSpinach Год назад +1

    Wrapping up a statistics PhD and I still come back to this video every few months to re-calibrate my intuition

  • @gamalieliissacnyambacha3029
    @gamalieliissacnyambacha3029 Год назад

    You clarify complex concepts to make them easier to understand; this will significantly help me in my Advanced Workbook assignment, thanks.

  • @i-fanlin568
    @i-fanlin568 2 года назад +3

    Your explanation of the proposal density is the best I ever found! Thank you so much for your sharing!

  • @bretasopik
    @bretasopik Год назад +1

    Amazing explanation! I usually do not comment on RUclips but here I make an exception. Good job!

  • @pxz3900
    @pxz3900 2 года назад

    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.

  • @itspulcio
    @itspulcio 2 года назад +1

    Love this explenation!

  • @איילתדמור
    @איילתדמור Год назад

    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!

  • @sxz452
    @sxz452 Год назад

    The best explanation of Metropolis Hastings on the internet.

  • @jacoblink4380
    @jacoblink4380 Год назад

    Mate, this is the most amazing and clear content re MCMC ive yet seen. incredible. thank you so much!

  • @michaelrainer7487
    @michaelrainer7487 2 года назад

    Ritvikmath is the only person who was able to finally explain Bayes to me. By far the best explanation I have ever seen. A+

  • @Feschmesser2
    @Feschmesser2 3 года назад +3

    Awesome explanation, best resource i have found to really understand the intuition behind MH. Thank for your effort!

  • @leoware9319
    @leoware9319 3 года назад +1

    This is so great. Best video I have found on this topic by far.

  • @raphaelbaur4335
    @raphaelbaur4335 3 года назад +4

    Amazing, deserves more views and could easily replace many of the lectures on MH out there!

  • @jaytimbadia2191
    @jaytimbadia2191 2 года назад +4

    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!

  • @Zooooooombie
    @Zooooooombie 2 года назад

    This succeeded for me where all other videos failed.. great explanation!

  • @xuxizhi6494
    @xuxizhi6494 10 месяцев назад

    Very clear explaination! Specifically, I love the intuition part at the end so much. Thanks for your excellent work!

    • @ritvikmath
      @ritvikmath  10 месяцев назад

      You're so welcome!

  • @MiaoQin-m2u
    @MiaoQin-m2u 3 месяца назад

    Thanks for sharing. I think I understand MH algorithm. You are so cool to explain profound theories in simple words!

  • @xinzhou4360
    @xinzhou4360 2 года назад

    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.

  • @timlonsdale
    @timlonsdale 2 года назад

    I'm binging your videos. God tier teaching!

  • @letranthu5165
    @letranthu5165 3 года назад +1

    The series of your videos is indeed amazing! Thank you so so much!

  • @thienle4434
    @thienle4434 3 года назад +1

    Wonderful job Ritvik. Thank you.

  • @crazyjerry543
    @crazyjerry543 3 года назад +3

    You definitely deserve more exposure!! Thanks a lot for these great explanations:)

  • @bassoonatic777
    @bassoonatic777 3 года назад

    Fantastic job. This is the best explanation and description of MH that I've ever heard.

  • @taotaotan5671
    @taotaotan5671 3 года назад +1

    Your channel is super helpful. I finally understand MCMC and successfully programmed!

  • @ZinzinsIA
    @ZinzinsIA Год назад

    Very very clear summary of MH algorithm with explanation of every step. Really great and helpful work, thanks a lot !

  • @moimonalisa5129
    @moimonalisa5129 2 года назад

    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..

  • @paintednow
    @paintednow 2 года назад +1

    Man, this is the best presentation of Metropolis-Hastings I have seen, yet. Respect - keep up the good work!

  • @rebeccalynch6315
    @rebeccalynch6315 3 года назад +2

    seriously you are saving me for upcoming exams
    thank you!

  • @GamerHDNL
    @GamerHDNL 2 года назад

    Truly increadible clarity, thank you very much!

  • @yianqian3709
    @yianqian3709 2 года назад

    Thank you so much!! This is the clearest explanation of MH I have ever seen.

  • @arianova5312
    @arianova5312 2 года назад

    Great video, the intuition part is amazing. Thanks!

  • @proxyme3628
    @proxyme3628 2 года назад +1

    Great video and explanation. Wish those articles and videos dumping math formulas watch this video and learn now to explain.

  • @stefanocarini8117
    @stefanocarini8117 9 месяцев назад +1

    Crystal clear! Thank you! :)

    • @ritvikmath
      @ritvikmath  9 месяцев назад

      Glad it was helpful!

  • @tannys
    @tannys 3 года назад +1

    best video on MH. you make a great teacher!

  • @tianlongwang7238
    @tianlongwang7238 Год назад

    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!!!

  • @sararay6263
    @sararay6263 2 года назад

    Great job Ritvik..such a cool explanation..love it!! Keep up the good work. Cheers!!

  • @learn5081
    @learn5081 3 года назад +2

    This is extremely helpful! Thank you so much!! Also I appreciate your sharing your own experience learning this!

  • @rikki146
    @rikki146 Год назад

    Your explanation is next level. Thank you very much!

  • @fengjeremy7878
    @fengjeremy7878 2 года назад

    The explanation of ituition is great!

  • @kathyker3498
    @kathyker3498 Месяц назад +1

    very well done... thank u!

  • @elenapatsalou9229
    @elenapatsalou9229 2 года назад

    You are great!!! keep going, finally, I understood the metropolis hastings algorithm idea xD

  • @diomerda111
    @diomerda111 2 года назад

    man, you are so gifted as a teacher, keep up the good work :)

  • @moeinpoi
    @moeinpoi 2 года назад

    Wish there was a triple-like button. Perfect explanation. Thanks a lot!

  • @AnganMitra007
    @AnganMitra007 2 года назад

    Great presentation and thanks for the intuition!

  • @claudiodicarlo2094
    @claudiodicarlo2094 2 года назад

    insane quality video

  • @leieiei14
    @leieiei14 3 года назад

    I found this video very helpful after I got confused in my course. Thank you very much!

  • @alexmonfis9305
    @alexmonfis9305 2 года назад

    I'm doing a master on data science and you are saving me on bayesian stats! Thanks

  • @IreneGao-n9i
    @IreneGao-n9i Год назад

    Thanks for your explanations!! Very useful and clear to help the understanding!!

  • @cynthpielin7334
    @cynthpielin7334 3 года назад

    Thank you! Really amazing lesson. I really appreciate the intuition part at the end!

  • @juanvillegas5580
    @juanvillegas5580 2 года назад

    Wow this was such a nice explanation, kudos!

  • @jackcashman1190
    @jackcashman1190 7 месяцев назад

    Incredible explanation.

  • @hannahnelson4569
    @hannahnelson4569 5 месяцев назад

    I learned something! Very good video!

  • @pavanpatel5050
    @pavanpatel5050 2 года назад

    Amazing explanation! MH was magic to me until I watched this! Thank you 🙏

  • @yukachuenkarenyu2862
    @yukachuenkarenyu2862 Год назад

    Thank you so so much that I finally understand metropolis hasting.🎉

  • @vasanthakumarg4538
    @vasanthakumarg4538 2 года назад

    Great work!

  • @andreaskrmmerbagge8831
    @andreaskrmmerbagge8831 7 месяцев назад

    Super well explained!

  • @matthewkumar7756
    @matthewkumar7756 3 года назад +2

    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!

    • @ritvikmath
      @ritvikmath  3 года назад +2

      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.

    • @matthewkumar7756
      @matthewkumar7756 3 года назад

      @@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.

    • @pradyumnadas6265
      @pradyumnadas6265 3 года назад

      @@ritvikmath Why not sample from a binomial distribution with p = 0.1?

  • @haresh5_5
    @haresh5_5 7 месяцев назад

    simply amazing

  • @duynguyen4154
    @duynguyen4154 3 года назад

    Wow, it's clear the best tutorial for me. Thanks

  • @polares01
    @polares01 3 года назад

    I love you man, thank you so much

  • @eprzepiora
    @eprzepiora Месяц назад +1

    this whole thing basically collapses to a moving mean distribution

  • @jiadong7873
    @jiadong7873 2 года назад

    this is an amzing video!

  • @DanyCywiak
    @DanyCywiak 7 месяцев назад

    Amazing and super helpful video! 👏🏻👏🏻

    • @ritvikmath
      @ritvikmath  7 месяцев назад

      Glad it was helpful!

  • @stefancovic5999
    @stefancovic5999 3 месяца назад

    Excellent video! Thank you!

  • @iskandarzulkifly4067
    @iskandarzulkifly4067 3 года назад +1

    this is an amazing content !!

  • @rhopsi-q6b
    @rhopsi-q6b Год назад

    Unbelievable well explained! Thx!

  • @HetirasDzn
    @HetirasDzn 3 года назад

    Thank you for the intuitive explanation.

  • @ingenierocivilizado728
    @ingenierocivilizado728 Год назад

    Very well explained! Thank you so much!

    • @cao2106
      @cao2106 Год назад

      Do you have any python code that uses MCMC to predict closing prices? Can I have it, thanks

  • @ginnyli2913
    @ginnyli2913 3 года назад +2

    Noticed your change from MAX to MIN at around 10:23. HAHAHAH, great move!

  • @xingyihu9948
    @xingyihu9948 2 года назад

    Thank you so much you explanation is the best!

  • @pan19682
    @pan19682 2 года назад

    it is a very amazing lecture. you are really a very good gifted teacher. pls make more videos go on educating us

  • @kristiapamungkas697
    @kristiapamungkas697 3 года назад +1

    Amazing explanation, thank you!

  • @talespereira6553
    @talespereira6553 2 года назад

    This video was perfect! so clear!

  • @MultiCraftTube
    @MultiCraftTube 3 года назад

    Thank you for this great explanation!

  • @fraserrennie
    @fraserrennie Год назад

    Outstanding video

  • @Opera-1553
    @Opera-1553 2 года назад +1

    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. 👏👏❣

  • @proxyme3628
    @proxyme3628 2 года назад

    At 10:20, A(a->b) = MAX(1, rfrg) is flipped to A(a->b) = MIN(1, rfrg), but still A(a->b) is MAX(1, f(b)/f(a)). Why? Should have been MIN(1, f(b)/f(a)) because going down should happen with the chance of f(b) / f(a) when f(b) < f(a)? Otherwise MAX(1, f(b)/f(a)) gives always TRUE and only climb happen. It would work for single mode but how about multi mode? To be able to handle multi mode, we should also go down, not just climb to find the global optima?

  • @michid8188
    @michid8188 3 года назад

    Great video!

  • @chunchen3450
    @chunchen3450 3 года назад

    Excellent video! Thank you very much

  • @sachinkshajil
    @sachinkshajil 3 года назад +1

    Thank you, so very much for this video. It is very very helpful.

  • @omarfaroukzouak8089
    @omarfaroukzouak8089 11 месяцев назад

    Thank you so much! That is truly helpful!

    • @ritvikmath
      @ritvikmath  11 месяцев назад +1

      You're so welcome!

  • @SophieStardollCT
    @SophieStardollCT Год назад

    This is awesome, thank you