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Incremental inference of collective graphical models

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Document pages: 6 pages

Abstract: We consider incremental inference problems from aggregate data for collectivedynamics. In particular, we address the problem of estimating the aggregatemarginals of a Markov chain from noisy aggregate observations in an incremental(online) fashion. We propose a sliding window Sinkhorn belief propagation(SW-SBP) algorithm that utilizes a sliding window filter of the most recentnoisy aggregate observations along with encoded information from discardedobservations. Our algorithm is built upon the recently proposed multi-marginaloptimal transport based SBP algorithm that leverages standard beliefpropagation and Sinkhorn algorithm to solve inference problems from aggregatedata. We demonstrate the performance of our algorithm on applications such asinferring population flow from aggregate observations.

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