Approximating MAPs for belief networks is NP-hard and other theorems
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This paper shows that approximating MAPs with a constant ratio bound is also NP-hard, and applies to networks with constrained in-degree and out-degree, applies to randomized approximation, and even applies if the ratio bound, instead of being constant, is allowed to be a polynomial function of various aspects of the network topology.About:
This article is published in Artificial Intelligence.The article was published on 1998-06-01 and is currently open access. It has received 118 citations till now. The article focuses on the topics: Bayesian network & Function (mathematics).read more
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Bayesian artificial intelligence
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TL;DR: This book discusses Bayesian Reasoning, Bayesian Network Applications, and Knowledge Engineering with Bayesian Networks I and II.
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Determinantal point processes for machine learning
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Coherence as constraint satisfaction
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Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
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References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Related Papers (5)
The computational complexity of probabilistic inference using Bayesian belief networks (research note)
Approximating probabilistic inference in Bayesian belief networks is NP-hard
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