Estimating an Activity Driven Hidden Markov Model

We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent $\mathit{\text{activity}}\mathit{\text{levels}}$ 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.