User Tools

Site Tools


Inference in Bayesian Networks (by Scott Davies and Andrew Moore)

Tutorial Slides by Andrew Moore

The majority of these slides were conceived and created by Scott Davies (scottd@cs.cmu,edu). Once you've got hold of a Bayesian Network, there remains the question of how you do inference with it. Inference is the operation in which some subset of the attributes are given to us with known values, and we must use the Bayes net to estimate the probability distribution of one or more of the remaining attributes. A typical use of inference is “I've got a temperature of 101, I'm a 37-year-old Male and my tongue feels kind of funny but I have no headache. What's the chance that I've got bubonic plague?”.

Download Tutorial Slides (PDF format)

Powerpoint Format: The Powerpoint originals of these slides are freely available to anyone who wishes to use them for their own work, or who wishes to teach using them in an academic institution. Please email Andrew Moore at if you would like him to send them to you. The only restriction is that they are not freely available for use as teaching materials in classes or tutorials outside degree-granting academic institutions.

Advertisment: I have recently joined Google, and am starting up the new Google Pittsburgh office on CMU's campus. We are hiring creative computer scientists who love programming, and Machine Learning is one the focus areas of the office. If you might be interested, feel welcome to send me email: .

tutorials/bayesinf.html.txt · Last modified: 2016/10/04 21:27 (external edit)