Approximate Inference; Part 1 of 200:00
Bayesian paradigm - 100:35
Bayesian paradigm - 202:38
Factor graphs - 103:11
Example factor graph05:09
Two tasks07:40
A (seemingly) intractable problem08:15
Clutter problem08:28
Exact posterior11:25
Representing posterior distributions13:09
Deterministic approximation14:45
Moment matching15:45
Today ; Tomorrow16:32
Best Gaussian by moment matching16:38
Strategy17:21
Approximating a single factor19:58
Graph - 119:59
Graph - 221:10
Single factor with Gaussian context25:04
Gaussian multiplication formula32:16
Approximation with narrow context34:03
Approximation with medium context37:32
Approximation with wide context38:03
Two factors41:48
Three factors45:59
Message Passing = Distributed Optimization48:17
Gaussian found by EP52:04
Other methods52:49
Accuracy53:17
Cost vs. accuracy55:16
Censoring example01:02:14
Time series problems01:06:29
Example: Tracking01:06:37
Factor graph - 201:07:07
Approximate factor graph01:08:20
Splitting a pairwise factor01:12:25
Splitting in context01:13:42
Sweeping through the graph - 101:20:43
Sweeping through the graph - 201:21:04
Sweeping through the graph - 301:21:20
Sweeping through the graph - 401:21:28
Example: Poisson tracking01:23:31
Poisson tracking model01:24:41
Factor graph - 301:25:07
Approximating a measurement factor01:26:08
Graph - 301:27:52
Graph - 401:29:37
Graph - 501:30:25
Graph - 601:33:42
