Probabilistic Inference in Influence Diagrams

TL;DR

This paper introduces a new method to convert influence diagrams into Bayesian network inference problems, making evaluation more efficient compared to previous methods.

Contribution

A novel reduction technique that simplifies influence diagram evaluation by producing easier Bayesian network inference problems.

Findings

The new reduction method results in easier BN inference problems.

It improves the efficiency of influence diagram evaluation.

Compared to previous methods, it reduces computational complexity.

Abstract

This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the mew method are much easier to solve than those induced by the two previous methods.