# Speeding up approximate MAP by applying domain knowledge about relevant variables

arXiv: https://arxiv.org/abs/2412.09264 · HTML: https://mlsanity.com/paper/2412.09264/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Approximating MAP using Local Search](https://mlsanity.com/paper/1301.2299/) (2013)

## Abstract (arXiv)

The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither observed nor part of the MAP variables) into a set of relevant variables, which are marginalized out, and irrelevant variables, which will be assigned a sampled value from their domain. In this study we explore whether knowledge about which variables are relevant for a particular query (i.e., domain knowledge) speeds up computation sufficiently to beat both exact MAP as well as approximate MAP while giving reasonably accurate results. Our results are inconclusive, but also show that this probably depends on the specifics of the MAP query, most prominently the number of MAP variables.