Lazy Evaluation of Symmetric Bayesian Decision Problems
This work addresses efficiency improvements for researchers and practitioners in Bayesian decision-making, but it appears incremental as it builds on existing lazy evaluation principles applied to a specific problem.
The paper tackles the computational intensity of solving symmetric Bayesian decision problems by proposing a lazy evaluation method that postpones computations to improve efficiency, as demonstrated through examples and comparisons with existing architectures like hugin and valuation-based systems.
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.