Spatio-Temporal Graphical Model Selection

Patrick L. Harrington Jr.; Alfred O. Hero III

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arXiv · 1004.2304

Spatio-Temporal Graphical Model Selection

Patrick L. Harrington Jr.·Alfred O. Hero III

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Abstract

We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered ($SIR$) model for interaction events fall into this framework. We pose the problem as a structure learning problem and solve it using an $\ell_1$-penalized likelihood convex program. We evaluate the solution on a simulated spread of infectious over a complex network. Our topology estimates outperform those of a standard spatial Markov random field graphical model selection using $\ell_1$-regularized logistic regression.

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BibTeX RIS

Patrick L. Harrington Jr., Alfred O. Hero III. 2010-04-14. Spatio-Temporal Graphical Model Selection. https://arxiv.org/abs/1004.2304

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