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We provide finite sample guarantees for the classical Chow-Liu algorithm (IEEE Trans.~Inform.~Theory, 1968) to learn a tree-structured graphical model of a distribution.
Approximating discrete probability distributions with dependence trees
C. K. Chow and C. N. Liu · 1968
Earlier work this paper cites.
Consistency of an estimate of tree-dependent probability distributions
C Chow and T Wagner · 1973
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A theory of the learnable
Leslie G Valiant · 1984
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Equivalence and synthesis of causal models
Thomas Verma and Judea Pearl · 1990
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Learning and robust learning of product distributions
Klaus-U Höffgen · 1993
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Efficient distribution-free learning of probabilistic concepts
Michael J Kearns and Robert E Schapire · 1994
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Toward efficient agnostic learning
Michael J Kearns, Robert E Schapire, and Linda M Sellie · 1994
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Learning bayesian networks is np-complete
David Maxwell Chickering · 1995
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Philosophical essays on probabilities, from 5th french edition published 1825, translated ai dale, 1995
PS Laplace · 1995
Earlier work this paper cites.
Graphical models
Steffen L Lauritzen · 1996
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The sample complexity of learning fixed-structure bayesian networks
Sanjoy Dasgupta · 1997
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An optimal approximation algorithm for bayesian inference
Paul Dagum and Michael Luby · 1997
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An accelerated chow and liu algorithm: Fitting tree distributions to high-dimensional sparse data
Marina Meila · 1999
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Learning with mixtures of trees
Marina Meila and Michael I Jordan · 2000
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Convergence properties of functional estimates for discrete distributions
András Antos and Ioannis Kontoyiannis · 2001
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Testing random variables for independence and identity
Tugkan Batu, Lance Fortnow, Eldar Fischer, Ravi Kumar, Ronitt Rubinfeld, and Patrick White · 2001
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Factor graphs and the sum-product algorithm
Frank R Kschischang, Brendan J Frey, and H-A Loeliger · 2001
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Learning markov networks: maximum bounded tree-width graphs
David R. Karger and Nathan Srebro · 2001
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Finding a path is harder than finding a tree
Christopher Meek · 2001
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Estimation of entropy and mutual information
Liam Paninski · 2003
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Maximum likelihood bounded tree-width markov networks
Nathan Srebro · 2003
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Pac-learning bounded tree-width graphical models
Mukund Narasimhan and Jeff Bilmes · 2004
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Learning factor graphs in polynomial time and sample complexity
Pieter Abbeel, Daphne Koller, and Andrew Y Ng · 2006
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Estimating the“wrong” graphical model: Benefits in the computation-limited setting
Martin J Wainwright · 2006
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Efficient principled learning of thin junction trees
Anton Chechetka and Carlos Guestrin · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
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A new approach for testing properties of discrete distributions
Ilias Diakonikolas and Daniel M. Kane · 2016
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Square hellinger subadditivity for bayesian networks and its applications to identity testing
Constantinos Daskalakis and Qinxuan Pan · 2017
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Introduction to Property Testing
Oded Goldreich · 2017
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Learning graphical models using multiplicative weights
Adam R. Klivans and Raghu Meka · 2017
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Testing conditional independence of discrete distributions
Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart · 2018
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Sample-optimal identity testing with high probability
Ilias Diakonikolas, Themis Gouleakis, John Peebles, and Eric Price · 2018
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