Approximate Inference
author:
Tom Minka,
Microsoft Research
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| Slides | |
| 0:00 | Approximate Inference; Part 1 of 2 |
| 0:35 | Bayesian paradigm - 1 |
| 2:38 | Bayesian paradigm - 2 |
| 3:11 | Factor graphs - 1 |
| 5:09 | Example factor graph |
| 7:40 | Two tasks |
| 8:15 | A (seemingly) intractable problem |
| 8:28 | Clutter problem |
| 11:25 | Exact posterior |
| 13:09 | Representing posterior distributions |
| 14:45 | Deterministic approximation |
| 15:45 | Moment matching |
| 16:32 | Today ; Tomorrow |
| 16:38 | Best Gaussian by moment matching |
| 17:21 | Strategy |
| 19:58 | Approximating a single factor |
| 19:59 | Graph - 1 |
| 21:10 | Graph - 2 |
| 25:04 | Single factor with Gaussian context |
| 32:16 | Gaussian multiplication formula |
| 34:03 | Approximation with narrow context |
| 37:32 | Approximation with medium context |
| 38:03 | Approximation with wide context |
| 41:48 | Two factors |
| 45:59 | Three factors |
| 48:17 | Message Passing = Distributed Optimization |
| 52:04 | Gaussian found by EP |
| 52:49 | Other methods |
| 53:17 | Accuracy |
| 55:16 | Cost vs. accuracy |
| 62:14 | Censoring example |
| 66:29 | Time series problems |
| 66:37 | Example: Tracking |
| 67:07 | Factor graph - 2 |
| 68:20 | Approximate factor graph |
| 72:25 | Splitting a pairwise factor |
| 73:42 | Splitting in context |
| 80:43 | Sweeping through the graph - 1 |
| 81:04 | Sweeping through the graph - 2 |
| 81:20 | Sweeping through the graph - 3 |
| 81:28 | Sweeping through the graph - 4 |
| 83:31 | Example: Poisson tracking |
| 84:41 | Poisson tracking model |
| 85:07 | Factor graph - 3 |
| 86:08 | Approximating a measurement factor |
| 87:52 | Graph - 3 |
| 89:37 | Graph - 4 |
| 90:25 | Graph - 5 |
| 93:42 | Graph - 6 |
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