• Skip to main content
  • Skip to footer

BRICKS

Bayesian Representation and Inference for Complex Knowledge Structuring

  • The BRICKS methodology
  • The BRICKS architecture
  • What are PRMs?
  • Documentation
  • Contact

What are PRMs?

Probabilistic relational models extend Bayesian networks with the concepts of objects, their properties, and relations between them. In a way, they are to Bayesian networks as relational logic is to propositional logic.

A PRM specifies a template for a probability distribution over a database. The template includes a relational component that describes the relational schema for our domain, and a probabilistic component that describes the probabilistic dependencies that hold in our domain. A PRM has a coherent formal semantics in terms of probability distributions over sets of relational logic interpretations. Given a set of ground objects, a PRM specifies a probability distribution over a set of interpretations involving these objects (and perhaps other objects as well).

A PRM, together with a particular database of objects and relations, defines a probability distribution over the attributes of the objects.

L. GETOOR, N. FRIEDMAN, D. KOLLER, A. PFEFFER AND B. TASKAR
HTTPS://AI.STANFORD.EDU/~KOLLER/PAPERS/GETOOR+AL:SRL07.PDF

Footer

Contact Us

Bayesia S.A.S.

Parc Cérès, Bâtiment N
21, rue Ferdinand Buisson
53810 Changé, France

+33 (0)2 43 49 75 69

Send us a message

About Us

Bayesia S.A.S. is a French software development company, founded in 2001 by Dr. Lionel Jouffe and Dr. Paul Munteanu, which specializes in artificial intelligence technology.

Bayesia’s software portfolio covers all aspects of decision support with Bayesian networks and includes BayesiaLab, BEST and BRICKS. Their spectrum ranges from applied research to the development and deployment of industrial applications.

Learn more

Copyright © 2023 · Bayesia · Log in