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stat.ME2016
Graphical Log-linear Models: Fundamental Concepts and Applications
Niharika Gauraha
We present a comprehensive study of graphical log-linear models for contingency tables. High dimensional contingency tables arise in many areas such as computational biology, colle…
stat.ME2016
Mutual Conditional Independence and its Applications to Inference in Markov Networks
Niharika Gauraha
The fundamental concepts underlying in Markov networks are the conditional independence and the set of rules called Markov properties that translates conditional independence const…
stat.ME2016
Model Selection for Graphical Log-linear Models: A Forward Model Selection Algorithm based on Mutual Conditional Independence
Niharika Gauraha
Model selection and learning the structure of graphical models from the data sample constitutes an important field of probabilistic graphical model research, as in most of the situ…