Inference for dynamic and latent variable models via iterated, perturbed Bayes maps
Edward L. Ionides, Dao Nguyen, Yves F. Atchadé, Stilian Stoev, Aaron A. King · Proceedings of the National Academy of Sciences · 2015
Significance Many scientific challenges involve the study of stochastic dynamic systems for which only noisy or incomplete measurements are available. Inference for partially observed Markov process models provides a framework for formulating and answering questions about these systems. Except when the system is small, or approximately linear and Gaussian, state-of-the-art statistical methods are required to make efficient use of available data. Evaluation of the likelihood for a partially observed Markov process model can be formulated as a filtering problem. Iterated filtering algorithms carry out repeated Monte Carlo filtering operations to maximize the likelihood. We develop a new theoretical framework for iterated filtering and construct a new algorithm that dramatically outperforms previous approaches on a challenging inference problem in disease ecology.