Learning Mixtures of Ising Models using Pseudolikelihood
Machine Learning
2015-06-09 v1 Machine Learning
Abstract
Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrated for Ising and Potts models on both synthetic and real data.
Keywords
Cite
@article{arxiv.1506.02510,
title = {Learning Mixtures of Ising Models using Pseudolikelihood},
author = {Onur Dikmen},
journal= {arXiv preprint arXiv:1506.02510},
year = {2015}
}