Estimation Of Time-Varying Mixture Models: An Application To Traffic Estimation

Time varying mixture models can be a useful tool for modelling complex data collections. However the additional complexity of letting the number of mixture components vary over time adds even more difficulty in inference of the distribution parameters. We propose the automatic hard EM algorithm to infer the parameters of these complex, time-varying mixture models. We demonstrate its performance using simulated and real data in a traffic flow estimation scenario.