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Estimating an Activity Driven Hidden Markov Model

2015-07-27 · David A. Meyer, Asif Shakeel

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.

📄 PDF Abstract BibTeX arXiv:1507.07495

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