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Estimating an Activity Driven Hidden Markov Model
Published 27 Jul 2015 in stat.ML, cs.DS, cs.LG, cs.SI, math.ST, and stat.TH | (1507.07495v1)
Abstract: We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent $\textit{activity levels}$ that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone records.
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