Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » Machine Learning Summer School 2009 - Cambridge

Markov Chain Monte Carlo

author:Iain Murray, Department of Computer Science, University of Toronto
published: Nov. 2, 2009,   recorded: August 2009,   views: 1669
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Slides

Slides
0:00 Markov chain Monte Carlo
0:32 A statistical problem
1:59 Simple Monte Carlo
3:37 Properties of Monte Carlo
4:35 A dumb approximation
5:56 Aside: don't always sample!
7:19 Eye-balling samples
9:42 Monte Carlo and Insomnia
10:20 Sampling from a Bayes net
11:20 Sampling the conditionals
12:04 Sampling from distributions - 1
13:07 Sampling from distributions - 2
13:50 Rejection sampling
15:36 Importance sampling
20:46 Importance sampling (2)
22:47 Summary so far - 1
23:58 Application to large problems - 1
25:21 Application to large problems - 2
27:24 Importance sampling weights
29:08 Metropolis algorithm
32:16 Markov chain Monte Carlo
35:43 Transition operators
42:47 Detailed Balance
46:14 Reverse operators
50:36 Metropolis - Hastings
55:54 Matlab/Octave code for demo
56:29 Step-size demo
63:45 Metropolis limitations
66:43 Combining operators
69:40 Gibbs sampling
71:13 "Routine" Gibbs sampling
77:34 Summary so far - 2

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Reviews and comments:

Comment1 Kathryn, January 18, 2010 at 12:12 a.m.:

Awesome, thanks to the lecturer and whoever taped it!

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