Learning Finite Mixture of Ising Graphical Models
Date of Award
Doctor of Philosophy
Kevin H. Lee, Ph.D.
Joshua D. Naranjo, Ph.D.
Yingying Zhang, Ph.D.
Jinseok Kim, Ph.D.
Expectation-maximization algorithm, heterogeneous conditional dependencies, mixture of Ising graphical models
The Ising model is valuable in examining complex interactions within a system, but its estimation is challenging. In this work, we proposed penalized likelihood procedures to infer conditional dependence structure when observed data come from heterogeneous resources in high-dimensional setting. The proposed method can be efficiently implemented by taking advantage of coordinate-ascent, minorization–maximization principles and EM algorithm. A BIC-type criterion will be utilized for the selection of the tuning parameter in the penalized likelihood approaches. The effectiveness of the proposed method is supported by simulation studies and a real-world example.
Gu, Chong, "Learning Finite Mixture of Ising Graphical Models" (2023). Dissertations. 3961.