Approximate MMAP by Marginal Search

Alessandro Antonucci, Thomas Tiotto

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We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models. The algorithm is based on a reduction of the task to a polynomial number of marginal inference computations. Given an input evidence, the marginals mass functions of the variables to be explained are computed. Marginal information gain is used to decide the variables to be explained first, and their most probable marginal states are consequently moved to the evidence. The sequential iteration of this procedure leads to a MMAP explanation and the minimum information gain obtained during the process can be regarded as a confidence measure for the explanation. Preliminary experiments show that the proposed confidence measure is properly detecting instances for which the algorithm is accurate and, for sufficiently high confidence levels, the algorithm gives the exact solution or an approximation whose Hamming distance from the exact one is small.
Originele taal-2English
TitelFLAIRS-33: The 33rd International Conference of the Florida Artificial Intelligence Research Society
SubtitelProceedings
UitgeverijarXiv
StatusPublished - 12-feb-2020
EvenementFLAIRS-33: The 33rd International Conference of the Florida Artificial Intelligence Research Society - North Miami Beach, United States
Duur: 17-mei-202020-mei-2020

Publicatie series

NaamProceedings of the AAAI Conference on Artificial Intelligence
ISSN van geprinte versie2159-5399

Conference

ConferenceFLAIRS-33
LandUnited States
Stad North Miami Beach
Periode17/05/202020/05/2020

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