Conditional Plausibility Measures and Bayesian Networks

TL;DR

This paper introduces a unified framework for algebraic conditional plausibility measures, encompassing various uncertainty models, and demonstrates their representation through Bayesian networks, enhancing understanding of probabilistic reasoning.

Contribution

It defines algebraic conditional plausibility measures and shows how they can be represented using Bayesian networks, unifying different uncertainty models.

Findings

Algebraic conditional plausibility measures encompass probability, ranking, and possibility measures.

Bayesian networks can represent these generalized measures.

The framework unifies various models of uncertainty.

Abstract

A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that algebraic conditional plausibility measures can be represented using Bayesian networks.