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- W814917766 abstract "Undirected graphical models, also known as Markov random fields, are widely used to model stochastic dependences among large collections of variables. We introduce a new method of estimating sparse undirected conditional independence graphs based on the score matching loss, introduced by Hyvarinen (2005), and subsequently extended in Hyvarinen (2007). The regularized score matching method we propose applies to settings with continuous observations and allows for computationally efficient treatment of possibly non-Gaussian exponential family models. In the well-explored Gaussian setting, regularized score matching avoids issues of asymmetry that arise when applying the technique of neighborhood selection, and compared to existing methods that directly yield symmetric estimates, the score matching approach has the advantage that the considered loss is quadratic and gives piecewise linear solution paths under $ell_1$-regularization. Under suitable irrepresentability conditions, we show that $ell_1$-regularized score matching is consistent for graph estimation in high-dimensional settings. Through numerical experiments and an application to RNSseq data, we confirm that regularized score matching achieves state-of-the-art performance in the Gaussian case and provides a valuable tool for computationally efficient inference in non-Gaussian graphical models." @default.
- W814917766 created "2016-06-24" @default.
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- W814917766 date "2015-07-02" @default.
- W814917766 modified "2023-09-27" @default.
- W814917766 title "High-dimensional inference of graphical models using regularized score matching" @default.
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