Paper
F. J. Díez and M. J. Druzdzel. Canonical probabilistic models for knowledge engineering. Technical Report CISIAD-06-01. UNED, Madrid, 2006.
Abstract
The hardest task in knowledge engineering for
probabilistic graphical models, such as Bayesian networks and influence
diagrams, is obtaining their numerical parameters. Models based on
acyclic directed graphs and composed of discrete variables, currently
most common in practice, require for every variable a number of
parameters that is exponential in the number of its parents in the
graph, which makes elicitation from experts or learning from databases
a daunting task. In this paper, we review the so called canonical models
whose main advantage is that they require much fewer parameters. We
propose a general framework for them, based on three categories:
deterministic models, ICI models, and simple canonical models. ICI
models rely on the concept of independence of causal influence, and
can be subdivided into noisy and leaky. We then analyze the most common
families of canonical models (the OR/MAX, the AND/MIN, and the noisy
XOR), generalizing them and offering criteria for applying them in
practice. We also briefly review temporal canonical models.
Javier Díez
/ Last update: April 26, 2007.