MCE Reasoning in Recursive Causal Networks

TL;DR

This paper introduces a probabilistic reasoning method using Minimum Cross Entropy within Recursive Causal Models, employing a specialized language for belief propagation and demonstrating its implementation and performance comparison.

Contribution

It presents a novel probabilistic reasoning approach based on MCE and RCM, with a new language BNDL for describing dependencies and correlations among variables.

Findings

Implemented BNDL interpreters in Prolog and C

Compared performance with existing methods

Demonstrated effective belief propagation

Abstract

A probabilistic method of reasoning under uncertainty is proposed based on the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal Model (RCM). The dependency and correlations among the variables are described in a special language BNDL (Belief Networks Description Language). Beliefs are propagated among the clauses of the BNDL programs representing the underlying probabilistic distributions. BNDL interpreters in both Prolog and C has been developed and the performance of the method is compared with those of the others.