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Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence 102 (1):21-38 (1998)
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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.
What do mirror neurons mirror?Sebo Uithol, Iris van Rooij, Harold Bekkering & Pim Haselager - 2011 - Philosophical Psychology 24 (5):607 - 623.
When Can Predictive Brains be Truly Bayesian?Mark Blokpoel, Johan Kwisthout & Iris van Rooij - 2012 - Frontiers in Psychology 3.
The complexity of approximating MAPs for belief networks with bounded probabilities.Ashraf M. Abdelbar, Stephen T. Hedetniemi & Sandra M. Hedetniemi - 2000 - Artificial Intelligence 124 (2):283-288.
References found in this work
Fusion, propagation, and structuring in belief networks.Judea Pearl - 1986 - Artificial Intelligence 29 (3):241-288.
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.
Finding MAPs for belief networks is NP-hard.Solomon Eyal Shimony - 1994 - Artificial Intelligence 68 (2):399-410.
A linear constraint satisfaction approach to cost-based abduction.Eugene Santos - 1994 - Artificial Intelligence 65 (1):1-27.
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