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Finding MAPs for belief networks is NP-hard
Artificial Intelligence 68 (2):399-410 (1994)
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Citations of this work
Rational analysis, intractability, and the prospects of ‘as if’-explanations.Iris van Rooij, Johan Kwisthout, Todd Wareham & Cory Wright - 2018 - Synthese 195 (2):491-510.
Bayesian Intractability Is Not an Ailment That Approximation Can Cure.Johan Kwisthout, Todd Wareham & Iris van Rooij - 2011 - Cognitive Science 35 (5):779-784.
Coherence measures and inference to the best explanation.David H. Glass - 2007 - Synthese 157 (3):275-296.
A probabilistic plan recognition algorithm based on plan tree grammars.Christopher W. Geib & Robert P. Goldman - 2009 - Artificial Intelligence 173 (11):1101-1132.
Approximating MAPs for belief networks is NP-hard and other theorems.Ashraf M. Abdelbar & Sandra M. Hedetniemi - 1998 - Artificial Intelligence 102 (1):21-38.
References found in this work
The computational complexity of probabilistic inference using bayesian belief networks.Gregory F. Cooper - 1990 - Artificial Intelligence 42 (2-3):393-405.
Approximating probabilistic inference in Bayesian belief networks is NP-hard.Paul Dagum & Michael Luby - 1993 - Artificial Intelligence 60 (1):141-153.
A linear constraint satisfaction approach to cost-based abduction.Eugene Santos - 1994 - Artificial Intelligence 65 (1):1-27.
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