Understanding Metropolis-Hastings algorithm

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  • Опубликовано: 23 дек 2024

Комментарии • 34

  • @sethjchandler
    @sethjchandler 2 года назад +7

    Can't believe how good your backwards handwriting is ;)

  • @justinxia9293
    @justinxia9293 4 года назад +8

    This is a pretty nice video thank you so much!!! I feel like I understood a lot of things much better after listening to the video. But there's also homework to prepare for if one wants to fully appreciate the content in the video. One should understand mostly about Bayesian statistics, and terms like normalizing factors. If not, checking around Wikipedia might be a good option.

  • @nleontis
    @nleontis 4 года назад +13

    How does this guy write backwards?

    • @lucas29476
      @lucas29476 3 года назад +6

      same question. I looked it up and apparently it involves writing on glass in front of camera + flipping the video horizontally.

  • @khldh
    @khldh 4 года назад +36

    but how did you write in reverse?

    • @frozenburrito9313
      @frozenburrito9313 4 года назад +13

      he didn't, they just reverse the video afterward

    • @purelogic4533
      @purelogic4533 4 года назад +2

      @@frozenburrito9313 that cant be right. What is filmed is a mirror reflection.

    • @frozenburrito9313
      @frozenburrito9313 4 года назад +1

      PureLogic right, that's the correct word

  • @MiikaMakiVL
    @MiikaMakiVL Год назад

    Very nice and conscise presentation, thanks!

  • @ecapanema
    @ecapanema 3 года назад +3

    Thank you. Great set you got there for presenting it, almost convinced me you're actually left-handed lololol

  • @cindyxu1100
    @cindyxu1100 3 года назад +1

    explained everything super clear, thank you

  • @gihanna
    @gihanna 2 года назад

    Brilliant explanation!!! this is the best one

  • @小江-j1i
    @小江-j1i 3 года назад +1

    Where is the next vedio?

  • @thefarrellgroup3633
    @thefarrellgroup3633 2 года назад +2

    are you writing backwards?! very nice video, thank you for sharing.

  • @freezeandfry
    @freezeandfry 4 года назад +6

    Great video! Still wondering, can you perhaps further explain why the terms that cancel are equal for a normal distribution at 7:21? Can't wait for the next video coming out!

    • @TimBate
      @TimBate 2 года назад +5

      A normal distribution is symmetric about the mean. So, to put some numbers on it, let's say that the proposed theta (theta_star) was one sigma to the right of theta_(i-1). This of course means that theta_(i-1) is one sigma to the left of theta_star. So q(theta_star | theta_(i-1)) is equal to the normal distribution density at +1 sigma. In the divisor, you have q(theta_(i-1) | theta_star) which is equal to the normal distribution density at -1 sigma. But the normal distribution is symmetric about 0, so those values are equal and cancel each other out.

  • @jacobmoore8734
    @jacobmoore8734 4 года назад +2

    Could you explain the intuitions behind the formula for alpha?

    • @johannesluttmer1285
      @johannesluttmer1285 3 года назад

      How about totalitair communism? ? Plain in side. .right in front of your 👀

    • @dominicj7977
      @dominicj7977 3 года назад

      Posterior probability of new theta / Posterior probability of old theta.
      You need to understand bayesian statistics for continuous distributions for this.

  • @ipuhbamrash6708
    @ipuhbamrash6708 4 года назад +1

    Do you have more video of the tutor??

  • @smartmarc333
    @smartmarc333 3 года назад

    Just a very good video!

  • @calc_5830
    @calc_5830 3 года назад

    where is the next?

  • @abdelkarimaths
    @abdelkarimaths 4 года назад

    can we have an example of programming this example on Matlab.

  • @mohammadghani1379
    @mohammadghani1379 2 года назад

    Dear Professor, what is "q" here?

    • @luiarthur
      @luiarthur 20 дней назад

      There's a slight abuse of notation. q(theta* | theta_{i-1}) means 2 things here. It means both the probability density function for theta* (the candidate value) given the current state of theta; and it also represents the distribution theta* | theta_{i-1}. As mentioned, a commonly used proposal distribution is the Normal distribution. q(theta* | theta _{i-1}) would then be the Normal probability density function with mean theta_{i-1}, i.e. the current value, evaluated at the candidate value theta*, which is also drawn from this Normal distribution. Note that a Normal distribution also needs a variance. That is a tricky thing to set, even when a Normal proposal is indeed suitable. One reason the Normal proposal is commonly used is that those q factors end up cancelling, due to symmetry, so you don't actually compute the proposal densities. Though, you still sample from the proposal distribution.

  • @fdsfkdj
    @fdsfkdj 3 года назад

    you got me at the first sentence

  • @JohnSmith-lf5xm
    @JohnSmith-lf5xm 4 года назад +1

    I still did not understand. I will wait for the next video. Thank you anyway !

    • @MachineLearningTV
      @MachineLearningTV  4 года назад +5

      Soon we will upload the next video

    • @maximmarchal9991
      @maximmarchal9991 4 года назад +1

      @@MachineLearningTV But when?! Great video by the way.

    • @johannesluttmer1285
      @johannesluttmer1285 3 года назад +1

      @@maximmarchal9991 just look at the movie Metropolis from 1927 or listen to welcome to the machine from pink Floyd and know what's going on. ..this are the bad guys. That's all

  • @CW-pe1ci
    @CW-pe1ci 3 года назад

    Nothing more than repeated the Wiki, you didn't specify what's theta_0, how the "draw" candidate, not even one example.

  • @donpkchannel7203
    @donpkchannel7203 3 года назад

    You miss an example. Without example this looks like science fiction

  • @zahrajalilpour9964
    @zahrajalilpour9964 4 года назад

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