 
Summary: ETHEM ALPAYDIN
© The MIT Press, 2010
alpaydin@boun.edu.tr
http://www.cmpe.boun.edu.tr/~ethem/i2ml2e
Lecture Slides for
Graphical Models
Aka Bayesian networks, probabilistic networks
Nodes are hypotheses (random vars) and the probabilities
corresponds to our belief in the truth of the hypothesis
Arcs are direct influences between hypotheses
The structure is represented as a directed acyclic graph
(DAG)
The parameters are the conditional probabilities in the
arcs (Pearl, 1988, 2000; Jensen, 1996; Lauritzen, 1996)
3Lecture Notes for E Alpaydin 2010 Introduction to Machine Learning 2e © The MIT Press (V1.0)
4
Causes and Bayes' Rule
Diagnostic inference:
Knowing that the grass is wet,
what is the probability that rain is
