| Texto completo | |
| Autor(es): |
Maua, Denis Deratani
Número total de Autores: 1
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| Tipo de documento: | Artigo Científico |
| Fonte: | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING; v. 68, p. 211-229, JAN 2016. |
| Citações Web of Science: | 3 |
| Resumo | |
Two important tasks in probabilistic reasoning are the computation of the maximum posterior probability of a given subset of the variables in a Bayesian network (MAP), and the computation of the maximum expected utility of a strategy in an influence diagram (MEU). Both problems are NPPP-hard to solve, and NP-hard to approximate when the treewidth of the underlying graph is bounded. Despite the similarities, researches on both problems have largely been conducted independently, with algorithmic solutions and insights designed for one problem not (trivially) transferable to the other one. In this work, we show constructively that these two problems are equivalent in the sense that any algorithm designed for one problem can be used to solve the other with small overhead. Moreover, the reductions preserve the boundedness of treewidth. Building on the known complexity of MAP on networks whose parameters are imprecisely specified, we show how to use the reductions to characterize the complexity of MEU when the parameters are set-valued. These equivalences extend the toolbox of either problem, and shall foster new insights into their solution. (C) 2015 Elsevier Inc. All rights reserved. (AU) | |
| Processo FAPESP: | 13/23197-4 - Algoritmos Eficientes para Tomada de Decisão sob Incerteza Baseada em Grafos |
| Beneficiário: | Denis Deratani Mauá |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |