Solving MAP Exactly using Systematic Search

TL;DR

This paper presents a new upper bound and a branch-and-bound algorithm for solving the MAP problem exactly in Bayesian networks, enabling solutions for larger and more complex networks than previously possible.

Contribution

It introduces a tighter upper bound on MAP probability and develops an exact solving algorithm that outperforms existing structure-based methods on large networks.

Findings

Can solve networks with constrained treewidth over 40

Outperforms existing methods in efficiency and scalability

Successfully computes exact MAP solutions for complex networks

Abstract

MAP is the problem of finding a most probable instantiation of a set of variables in a Bayesian network given some evidence. Unlike computing posterior probabilities, or MPE (a special case of MAP), the time and space complexity of structural solutions for MAP are not only exponential in the network treewidth, but in a larger parameter known as the "constrained" treewidth. In practice, this means that computing MAP can be orders of magnitude more expensive than computing posterior probabilities or MPE. This paper introduces a new, simple upper bound on the probability of a MAP solution, which admits a tradeoff between the bound quality and the time needed to compute it. The bound is shown to be generally much tighter than those of other methods of comparable complexity. We use this proposed upper bound to develop a branch-and-bound search algorithm for solving MAP exactly. Experimental results demonstrate that the search algorithm is able to solve many problems that are far beyond the reach of any structure-based method for MAP. For example, we show that the proposed algorithm can compute MAP exactly and efficiently for some networks whose constrained treewidth is more than 40.