TY - T1 - Learning pairwise Markov Network structures with logistic regression SN - / UR - URN:NBN:fi:hulib-201712125847; http://hdl.handle.net/10138/229585 T3 - A1 - Kuronen, Juri A2 - PB - Helsingin yliopisto Y1 - 2017 LA - eng AB - This Master’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 ... VO - IS - SP - OP - KW - Markov network; Markov network structure learning; logistic regression; pseudo-likelihood; Bayesian information criterion; Tilastotiede; Statistics; Statistik N1 - PP - ER -