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The 13th International Conference on Knowledge Discovery and Data Mining

Learning Bayesian Networks

author: Richard E. Neapolitan, Northeastern Illinois University

Description

Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. The 1990's saw the emergence of excellent algorithms for learning Bayesian networks from passive data.

I will discuss the constraint-based learning method using an intuitive approach that concentrates on causal learning. Then I will discuss the Bayesian approach with some simple examples. I will show how, using the Bayesian approach, we can even learning something about causal influences from passive data on two variables. Finally, I will show some applications to finance and marketing.

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Slides
0:03 Statistical Causality
1:23 slide 2
2:46 A common way to learn (perhaps define) causation is via manipulation experiments (non-passive data)
4:30 slide 4
5:03 slide 5
5:33 slide 6
6:20 slide 7
7:22 slide 8
8:05 Causal Graphs
10:36 The Causal Markov Assumption
12:41 Examples
18:35 slide 12
20:14 slide 13
24:18 slide 14
29:24 Experimental evidence for the Causal Markov Assumption
29:42 Exceptions to the Causal Markov Assumption
29:45 1. Hidden common causes
31:21 2. Causal feedback
31:44 3. Selection bias
34:22 4. The entities in the population are units of time
35:24 slide 21
35:41 slide 22
39:31 Causal Faithfulness Assumption
40:43 Exceptions to the Causal Faithfulness Assumption
41:13 Learning Causal Influences Under the Causal Faithfulness Assumption
41:33 slide 26
42:32 Example
43:18 slide 28
43:43 Example 1
45:18 Example 2
46:18 Example 3
51:48 Example 4
53:29 Theorem
54:14 Example 5
57:39 How much data do we need?
59:17 Empirical Results
60:35 Conflicting Empirical Results

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Reviews and comments:

Comment1 Eugen Hotwagner, December 13, 2007 at 3:49 p.m.:

This is a very good introduction to causality and learning bayesian networks. there are many examples which are explained in detail. the lecture is also a very good introduction and supplement to neapolitans book "learning bayesian networks" which can be found here: http://www.amazon.com/Learning-Bayesi... the book is very good but also very expensive. i bought it and kind of regretted it later because of the price. combined with this lecture it might be worth it :)


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