A Dynamic Approach to Probabilistic Inference

TL;DR

This paper introduces a framework for building Bayesian networks dynamically using a background knowledge base of schemata, enabling flexible and context-specific probabilistic inference.

Contribution

It presents a novel method for constructing Bayesian networks from schemata, separating general properties from individual-specific knowledge for dynamic inference.

Findings

Framework for dynamic Bayesian network construction

Separation of general and individual-specific knowledge

Potential applications in flexible probabilistic reasoning

Abstract

In this paper we present a framework for dynamically constructing Bayesian networks. We introduce the notion of a background knowledge base of schemata, which is a collection of parameterized conditional probability statements. These schemata explicitly separate the general knowledge of properties an individual may have from the specific knowledge of particular individuals that may have these properties. Knowledge of individuals can be combined with this background knowledge to create Bayesian networks, which can then be used in any propagation scheme. We discuss the theory and assumptions necessary for the implementation of dynamic Bayesian networks, and indicate where our approach may be useful.