Sparsistent Estimation of Time-Varying Discrete Markov Random Fields

Mladen Kolar; Eric P. Xing

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arXiv · 0907.2337

Sparsistent Estimation of Time-Varying Discrete Markov Random Fields

Mladen Kolar·Eric P. Xing

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Abstract

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. This work complements previous empirical findings by providing sound theoretical guarantees for the proposed estimation procedure. For completeness, we include numerical simulations in the paper.

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BibTeX RIS

Mladen Kolar, Eric P. Xing. 2013-04-02. Sparsistent Estimation of Time-Varying Discrete Markov Random Fields. https://arxiv.org/abs/0907.2337

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