Operations Research
Probabilistic inference and influence diagrams
Operations Research
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Expert Systems
Decomposition of Multidimensional Distributions Represented by Perfect Sequences
Annals of Mathematics and Artificial Intelligence
Marginalization in Composed Probabilistic Models
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Composition of probability measures on finite spaces
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
This paper is a short review and comparison of two probabilistic models for uncertain knowledge representation: Bayesian networks and compositional models. We have chosen these two approaches because they represent the same class of distributions and because they are typical representatives of the approaches using conditional (for Bayesian networks) and unconditional (for compositional models) distributions as basic building blocks for model construction. The comparison will be made from the viewpoint of partial knowledge processing, in particular. Here we have in mind not only their capability to create global models from systems of pieces of local knowledge but most of all their efficiency to infer new pieces of local knowledge, different from those forming a generating (input) system.