Article ID: | iaor20116984 |
Volume: | 71 |
Issue: | 1 |
Start Page Number: | 33 |
End Page Number: | 62 |
Publication Date: | Jul 2011 |
Journal: | Theory and Decision |
Authors: | Gonzales Christophe, Wuillemin Pierre-Henri |
Keywords: | behaviour |
Probabilistic Relational Models (PRMs) are a framework for compactly representing uncertainties (actually probabilities). They result from the combination of Bayesian Networks (BNs), Object‐Oriented languages, and relational models. They are specifically designed for their efficient construction, maintenance and exploitation for very large scale problems, where BNs are known to perform poorly. Actually, in large‐scale problems, it is often the case that BNs result from the combination of patterns (small BN fragments) repeated many times. PRMs exploit this feature by defining these patterns only once (the so‐called PRM’s