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

Introduction To Bayesian Inference

author:Christopher Bishop, Microsoft Research
published: Nov. 2, 2009,   recorded: August 2009,   views: 3265
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Slides

Slides
0:00 INTRODUCTION TOBAYESIAN INFERENCE –PART 1
1:00 Please ask questions
1:24 Research
1:34 First Generation
3:13 Second Generation
4:02 Third Generation
5:37 Probability Theory
6:43 The Rules of Probability
7:12 Bayes’ Theorem
7:54 Oranges and Apples
9:41 Probability Densities
10:33 Bayesian Inference
13:14 Why is prior knowledge important?
15:10 Probabilistic Graphical Models
17:30 Decomposition
19:16 Directed Graphs
24:29 MAAS
25:56 Graph - 1
27:35 Graph - 2
29:55 Graph - 3
31:36 Graph - 4
33:18 Factor Graphs
34:10 From Directed Graph to Factor Graph
34:51 Inference on Graphs
35:48 Factor Trees: Separation
40:05 Messages: From Factors To Variables
41:05 Messages: From Variables To Factors
46:30 What if the graph is not a tree?
47:02 What if marginalisations are not tractable?
49:14 Illustration: Bayesian Ranking
50:58 Two Player Match Outcome Model
53:44 Two Team Match Outcome Model
54:33 Multiple Team Match Outcome Model
54:44 Graph - 5
56:00 Graph - 6
57:57 Skill Dynamics
62:50 TrueSkillTM
65:04 Graph - 7
66:57 infer.net
67:32 Infer.Net demonstration
67:41 Graph - 8

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Reviews and comments:

Comment1 Roger Orobi, November 17, 2009 at 6:58 p.m.:

I liked the slides on binary variable - distribution of placeba/Treated relationship of drugs. The question is: how can it be further studied and quantified to be made a standard measure to treat patients?

Comment2 murugan manoj, January 23, 2010 at 8:08 a.m.:

this is really good 4 me....

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