A Scheme for Approximating Probabilistic Inference
This work addresses probabilistic inference tasks like belief updating for researchers, but it appears incremental as it builds on existing bucket elimination methods.
The paper tackles the problem of probabilistic inference by proposing a class of approximation algorithms based on bucket elimination, which allows adjustable accuracy and efficiency, and provides preliminary empirical evaluation on randomly generated networks.
This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approximation for several tasks: finding the most probable explanation, belief updating and finding the maximum a posteriori hypothesis. We identify regions of completeness and provide preliminary empirical evaluation on randomly generated networks.