Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks
نویسندگان
چکیده
منابع مشابه
Stochastic Local Search for Solving the Most Probable Explanation Problem in Bayesian Networks
In this thesis, we develop and study novel Stochastic Local Search (SLS) algorithms for solving the Most Probable Explanation (MPE) problem in graphical models, that is, to find the most probable instantiation of all variables V in the model, given the observed values E = e of a subset E of V. SLS algorithms have been applied to the MPE problem before [KD99b, Par02], but none of the previous SL...
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In addition to computing the posterior distributions for hidden variables in Bayesian networks, one other important inference task is to find the most probable explanation (MPE). MPE provides the most likely configurations to explain away the evidence and helps to manage hypotheses for decision making. In recent years, researchers have proposed a few methods to find the MPE for discrete Bayesia...
متن کاملMost Relevant Explanation in Bayesian Networks
A major inference task in Bayesian networks is explaining why some variables are observed in their particular states using a set of target variables. Existing methods for solving this problem often generate explanations that are either too simple (underspecified) or too complex (overspecified). In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial ins...
متن کاملStochastic Local Search for Bayesian Networks
The paper evaluates empirically the suitability of Stochastic Local Search algorithms (SLS) for nding most probable explanations in Bayesian networks. SLS algorithms (e.g., GSAT, WSAT [16]) have recently proven to be highly e ective in solving complex constraint-satisfaction and satis ability problems which cannot be solved by traditional search schemes. Our experiments investigate the applicab...
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An overview is given of definitions and complexity results of a number of variants of the problem of probabilistic inference of the most probable explanation of a set of hypotheses given observed phenomena.
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2011
ISSN: 1041-4347
DOI: 10.1109/tkde.2010.98