Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
This is an incremental review paper summarizing existing methods for a domain-specific problem in statistical learning.
This paper reviews methodologies for learning sparse structural changes in high-dimensional Markov Networks, which capture alterations in variable interactions across different regimes without needing to model the individual networks.
Recent years have seen an increasing popularity of learning the sparse \emph{changes} in Markov Networks. Changes in the structure of Markov Networks reflect alternations of interactions between random variables under different regimes and provide insights into the underlying system. While each individual network structure can be complicated and difficult to learn, the overall change from one network to another can be simple. This intuition gave birth to an approach that \emph{directly} learns the sparse changes without modelling and learning the individual (possibly dense) networks. In this paper, we review such a direct learning method with some latest developments along this line of research.