Maximum likelihood bounded tree-width Markov networks
TLDR
It is proved that the problem of projecting a distribution onto (or finding a maximum likelihood distribution among) Markov networks of bounded tree-width is NP-hard to solve exactly and an approximation algorithm with a provable performance guarantee is provided.About:
This article is published in Artificial Intelligence.The article was published on 2003-01-01 and is currently open access. It has received 117 citations till now. The article focuses on the topics: Expectation–maximization algorithm & Markov model.read more
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Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
Mark Cummins,Paul Newman +1 more
TL;DR: A probabilistic approach to the problem of recognizing places based on their appearance that can determine that a new observation comes from a previously unseen place, and so augment its map, and is particularly suitable for online loop closure detection in mobile robotics.
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Bayesian Reasoning and Machine Learning
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High-dimensional Ising model selection using ${\ell_1}$-regularized logistic regression
TL;DR: It is proved that consistent neighborhood selection can be obtained for sample sizes $n=\Omega(d^3\log p)$ with exponentially decaying error, and when these same conditions are imposed directly on the sample matrices, it is shown that a reduced sample size suffices for the method to estimate neighborhoods consistently.
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High-dimensional Ising model selection using ℓ1-regularized logistic regression
TL;DR: In this paper, the problem of estimating the graph associated with a binary Ising Markov random field is considered, where the neighborhood of any given node is estimated by performing logistic regression subject to an l 1-constraint.
References
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Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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The Probabilistic Method
TL;DR: A particular set of problems - all dealing with “good” colorings of an underlying set of points relative to a given family of sets - is explored.
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Approximating discrete probability distributions with dependence trees
TL;DR: It is shown that the procedure derived in this paper yields an approximation of a minimum difference in information when applied to empirical observations from an unknown distribution of tree dependence, and the procedure is the maximum-likelihood estimate of the distribution.