A Fusion Algorithm for Solving Bayesian Decision Problems
This work addresses decision-making under uncertainty for researchers in AI and statistics, but it appears incremental as it hybridizes existing methods.
The paper tackles Bayesian decision problems by representing them as valuation-based systems and applying a fusion algorithm that combines local computational methods for marginals and discrete optimization, resulting in a new solution approach.
This paper proposes a new method for solving Bayesian decision problems. The method consists of representing a Bayesian decision problem as a valuation-based system and applying a fusion algorithm for solving it. The fusion algorithm is a hybrid of local computational methods for computation of marginals of joint probability distributions and the local computational methods for discrete optimization problems.