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  -