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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