  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.
