Markov Chain Monte Carlo

author: Iain Murray, Department of Computer Science, University of Toronto

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*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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