## Learning Bayesian Networks

published: Aug. 12, 2007, recorded: August 2007, views: 13347

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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:

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 :)

S.Gayathri, June 26, 2012 at 12:20 p.m.:Thanks for the presenattion sir its really helpful

John W. Brooks, October 4, 2012 at 4:55 a.m.:Outstanding Lecture! Thanks very much!

JWB

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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