Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments

TL;DR

This paper introduces a flexible probabilistic logic framework combining propositional and relational Bayesian networks with imprecise and qualitative assessments, supported by new inference algorithms demonstrating superior empirical performance.

Contribution

It presents a novel representation language integrating propositional and relational Bayesian networks with imprecise and qualitative probabilities, along with new inference algorithms.

Findings

New inference algorithms outperform existing methods

Framework effectively handles diverse probabilistic assessments

Empirical results demonstrate improved efficiency

Abstract

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.