Learning Bayesian Networks

author: Richard E. Neapolitan, Northeastern Illinois University
published: Aug. 12, 2007,   recorded: August 2007,   views: 13294
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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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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 :)


Comment2 S.Gayathri, June 26, 2012 at 12:20 p.m.:

Thanks for the presenattion sir its really helpful


Comment3 John W. Brooks, October 4, 2012 at 4:55 a.m.:

Outstanding Lecture! Thanks very much!

JWB


Comment4 G Costantini, December 13, 2012 at 5:21 p.m.:

I found this lecture very interesting and very helpful, thank you

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