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arXiv · 1206.6420

Distributed Parameter Estimation via Pseudo-likelihood

Qiang Liu·Alexander Ihler
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Abstract

Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a number of combination methods, and provide both theoretical and experimental analysis. We show that simple linear combination or max-voting methods, when combined with second-order information, are statistically competitive with more advanced and costly joint optimization. Our algorithms have many attractive properties including low communication and computational cost and "any-time" behavior.

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BibTeXRIS

Qiang Liu, Alexander Ihler. 2012-06-27. Distributed Parameter Estimation via Pseudo-likelihood. https://arxiv.org/abs/1206.6420

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