Sparsistent Estimation of Time-Varying Discrete Markov Random Fields

Understand

Network models have been popular for modeling and representing complex relationships and dependencies between observed variables.

• When data comes from a dynamic stochastic process, a single static network model cannot adequately capture transient dependencies, such as, gene regulatory dependencies throughout a developmental cycle of an organism.
• Kolar et al (2010b) proposed a method based on kernel-smoothing l1-penalized logistic regression for estimating time-varying networks from nodal observations collected from a time-series of observational data.
• In this paper, we establish conditions under which the proposed method consistently recovers the structure of a time-varying network.