Probabilistic relational model

A Probabilistic relational model (PRM) is the counterpart in relational learning of a Bayesian network. Probabilistic Relational Models (PRMs) are a language based on relational logic for describing statistical models of structured data. In addition to providing a sound and coherent foundation for dealing with the noise and uncertainty encountered in most real-world domains, the models themselves can be learned directly from an existing database or knowledge base using well-founded statistical techniques. PRMs model compex domains in terms of entities, their properties, and the relations between them. These models represent the uncertainty over the properties of an entity, capturing its probabilistic dependence both on other properties of that entity and on properties of related entities. PRMs can even represent uncertainty over the relational structure itself. PRMs provide a new framework for relation data mining, and offer new challenges for the endeavor of learning relational models for real-world domains.