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T1 - Learning pairwise Markov Network structures with logistic regression
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UR - URN:NBN:fi:hulib-201712125847; http://hdl.handle.net/10138/229585
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A1 - Kuronen, Juri
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PB - Helsingin yliopisto
Y1 - 2017
LA - eng
AB - This Masterâ€™s thesis introduces a new score-based method for learning the structure of a pairwise Markov network without imposing the assumption of chordality on the underlying graph structure by approximating the joint probability distribution using the popular pseudo-likelihood framework. Together with the local Markov property associated with the Markov network, the joint probability distribution is decomposed into node-wise conditional distributions involving only a tiny subset of variables ...
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KW - Markov network; Markov network structure learning; logistic regression; pseudo-likelihood; Bayesian information criterion; Tilastotiede; Statistics; Statistik
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