Transcription of 2 Graphical Models in a Nutshell
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2 Graphical Models in a Nutshell
2 Graphical Models in a Nutshell Daphne Koller, Nir Friedman, Lise Getoor and Ben Taskar Probabilistic Graphical Models are an elegant framework which combines uncer- tainty (probabilities) and logical structure (independence constraints) to compactly represent complex, real-world phenomena. The framework is quite general in that many of the commonly proposed statistical Models (Kalman lters, hidden Markov Models , Ising Models ) can be described as Graphical Models . Graphical Models have enjoyed a surge of interest in the last two decades, due both to the exibility and power of the representation and to the increased ability to e ectively learn and perform inference in large networks.
the distributions via a tree structure (called, appropriately enough, tree-structured CPDs), or using an even more compact representationsuch as anoisy-OR ornoisy-MAX. Example 2.1 Consider the simple Bayesian network shown in figure 2.1. This is a toy example indicating the interactions between two potential diseases, pneumonia and tuber-culosis.
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