Lazy Evaluation of Symmetric Bayesian Decision Problems

TL;DR

This paper introduces a lazy evaluation approach to efficiently solve symmetric Bayesian decision problems by postponing computations, demonstrating improved performance over existing methods through examples and comparisons.

Contribution

It presents a novel lazy evaluation method tailored for symmetric Bayesian decision problems, enhancing computational efficiency over traditional algorithms.

Findings

Significant efficiency gains demonstrated in examples

Lazy evaluation outperforms Hugin and valuation-based systems

Method effectively reduces computational overhead

Abstract

Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the hugin and valuation-based systems architectures for solving symmetric Bayesian decision problems.