for too long. Second, thermal lag limits response speed and thus precludes aggressive temperature set-back. Addressing both schedule inaccuracy and thermal lag requires a stochastic occupancy model and a control scheme that can use it effectively.

Considerable research effort has been directed toward occupancy detection and modeling. Work on detection has focused on boosting accuracy through sensor fusion using probabilistic, neural, or utility networks [@lam2009occupancy; @dodier2006building; @meyn2009sensor; @modelingCountData]. Agent-based models have been used to predict movement within buildings [@liao2012agent; @erickson2009energy], as have Markov chains [@erickson2010occupancy; @page2008generalised; @dong2011integrated]. Erickson and Dong, for example, considered rooms to be Markov states and movements among them to be transitions in order to predict persons’ behavior, while Dong and Lam [@dong2014real] used a semi-Markov model to merge multiple sensor streams into an occupant count estimate. The simpler Page model considered boolean occupancy (occupied or vacant) under a time-heterogeneous Markov chain to generate realistic simulation input data, rather than for on-line forecasting [@page2008generalised].

With the exception of the Page model, the above efforts have found use in heuristic [@erickson2011observe; @selfprogthermostat; @goyal2013occupancy] or model predictive control (MPC) schemes [@dong2014real; @Oldewurtel201215; @goyal2013occupancy], but they face barriers to widespread usage. Most notably, where authors have used MPC, they have 