Inference of Aggregate Hidden Markov Models With Continuous Observations

We consider a class of inference problems for large populations where each individual is modeled by the same hidden Markov model (HMM). We focus on aggregate inference problems in HMMs with discrete state space and continuous observation space. The continuous observations are aggregated in a way such that the individuals are indistinguishable from measurements. We propose an aggregate inference algorithm called continuous observation collective forward-backward algorithm. It extends the recently proposed collective forward-backward algorithm for aggregate inference in HMMs with discrete observations to the case of continuous observations.