Maximum likelihood bounded tree-width Markov networks
نویسندگان
چکیده
منابع مشابه
Maximum Likelihood Bounded Tree-Width Markov Networks
We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution among) Markov networks of bounded tree-width. By casting it as the combinatorial optimization problem of finding a maximum weight hypertree, we prove that it is NP-hard to solve exactly and provide an approximation algorithm with a provable performance guarantee.
متن کاملMethods and Experiments With Bounded Tree-width Markov Networks
Markov trees generalize naturally to bounded tree-width Markov networks, on which exact computations can still be done efficiently. However, learning the maximum likelihood Markov network with tree-width greater than 1 is NP-hard, so we discuss a few algorithms for approximating the optimal Markov network. We present a set of methods for training a density estimator. Each method is specified by...
متن کاملMethods and Experiments With Bounded Tree-width Markov Networks
Markov trees generalize naturally to bounded tree-width Markov networks, on which exact computations can still be done efficiently. However, learning the maximum likelihood Markov network with tree-width greater than 1 is NP-hard, so we discuss a few algorithms for approximating the optimal Markov network. We present a set of methods for training a density estimator. Each method is specified by...
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We show how a graphical model learning problem can be presented as a purely combinatorial problem. This allows us to analyze the computational hardness of the learning problem, and devise global optimization algorithms with proven performance guarantees. Markov networks are a class of graphical models that use an undirected graph to capture dependency information among random variables. Of part...
متن کاملMaximum Likelihood Markov Hypertrees
One popular class of such models are Markov networks, which use an undirected graph to represent dependencies among variables. Markov networks of low tree-width (i.e. having a triangulation with small cliques ) allow efficient computations, and are useful as learned probability models [8]. A well studied case is that in which the dependency structure is known in advance. In this case the underl...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2003
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(02)00360-0