An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation

27 Feb 2017 Sinong Geng Zhaobin Kuang David Page

The pseudo-likelihood method is one of the most popular algorithms for learning sparse binary pairwise Markov networks. In this paper, we formulate the $L_1$ regularized pseudo-likelihood problem as a sparse multiple logistic regression problem... (read more)

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Methods used in the Paper

Logistic Regression
Generalized Linear Models