An Algorithm for Computing Probabilistic Propositions

TL;DR

This paper introduces an algorithm for computing probabilistic propositions that leverages an external routine for probability calculations, enabling belief network algorithms to handle complex probabilistic queries efficiently in many cases.

Contribution

It presents a novel method that integrates an external probability routine into belief network algorithms to compute general probabilistic propositions.

Findings

Method is polynomial time for common query types

Enables belief networks to handle complex probabilistic propositions

Worst-case complexity remains exponential in query size

Abstract

A method for computing probabilistic propositions is presented. It assumes the availability of a single external routine for computing the probability of one instantiated variable, given a conjunction of other instantiated variables. In particular, the method allows belief network algorithms to calculate general probabilistic propositions over nodes in the network. Although in the worst case the time complexity of the method is exponential in the size of a query, it is polynomial in the size of a number of common types of queries.