Gibbs sampling

A minilecture describing Gibbs sampling.

[TheDebidatta]

This is the clearest explanation of Gibbs sampling out there. Thanks.

[jacobschultz]

Seeing the algorithm "walk" around the plane really made it click for me. Also, this made clear why we need to find every single conditional first. Thank you for the great work!

[TheAIEpiphany]

The example with the 2D Gaussian was invaluable to ground my understanding - thank you!

[tg]

Clear explanation, clear voice, clear slides. Good Job! Thanks!

[jiagengliu]

Just wondering if randomizing the order of sampling (like instead of going \theta_1, ... \theta_K, you do a random permutation of K) will help here? Is there a particular reason why we sample in this order?

[jessicas]

Amazing video! I was struggling with it and now I understand. Thank you so much!

[bunnysm]

Thank you very much! This is nicely and simply explained.

[GooseGood]

Gibbs sampling is just one special example of MH

[atjebb]

Thanks for the great and clear video! One question though: starting at 3:11, why do the normal conditional distributions have mean of rho*theta and variance of [1-rho^2]? Where is this parameterization coming from?

[ZbiggySmall]

Thanks for the video. I understood the concept but I am not an expert in probability. I know what conditional probability is. I am still struggling to figure out what it means when you sample Theta_1 given Theta_2, ..., Theta_K etc. and would not be able to explain why it's working if someone asked me. In the example of p(Theta) ~ N(0, Sigma), Theta_1 ~ N(ro x Theta_2(0), [1 - ro^2]). Here the covariance matrix became variance [1 - ro^2] and mean became ro x Theta_2(0). Where does it comes from?

[UlrichArmel]

Are all the samples accepted contrary to metropolis Hastings algorithm? Cool video

[mingyanchu]

very good explanation!! really helpful, thanks:)

[philipruijten]

Great explenation! Not to nitpick but from 2.14 the f(\theta) should be p(\theta) or am I looking at it the wrong way ? If not, what's the f ? 
Nonetheless really awesome explenation! Thanks! 

[ccuuttww]

I think the vector theta N contains Theta 1 and Theta 2 is a marginal distribution of Bi variate Normal when we plot a histogram with Theta 1 data
what it tell us ? The mean of Theta 1 is the value we want to estimate?

[weixuanli]

Nice lecture. Pretty clear explanation

[jacobmoore]

Btw, how did you derive the conditional distributions from the joint? When I wrote out the full analytic form of the joint PDF divided by one of the single variable PDFs, the equation did not simplify easily.

[Xnaarkhoo]

I have seen some paper and codes where they have used more complicated conditional probability. e.g sometimes, some pieces of their conditional probability contain gamma distribution. can you make some comment on how to advance a gibbs sampling ?

[boshranabaei]

Thanks a lot. Very good explanation.

[Carutsu]

I'm a bit too green on this so sorry, but how did you construct the conditioning? ie how did you go from theta_1|theta_2 to N(pTheta_1, i=p2)
EDIT: should've researched before, this seems like a solved problem, just plug the numbers.

[SrikantGadicherla]

If possible can you please share the link for ppts. Thanks!