(734) 615.3332
Methodologies: Statistical Inference, Statistical Modeling, Statistics, Time Series Analysis Relevant Projects:

Iterated filtering: new theory, algorithms and applications (NSF funded, role: PI).

Edward Ionides

Professor, Statistics, College of Literature, Science, and the Arts

Time series analysis with applications to ecology, epidemiology, health economics, cell motion and neuroscience. Methodological work on inference for partially observed stochastic dynamic systems.

The IF2 algorithm (Ionides, EL, D Nguyen, Y Atchade, S Stoev and AA King., 2015, "Inference for dynamic and latent variable models via iterated, perturbed Bayes maps," PNAS 112:719-724).

The IF2 algorithm (Ionides, EL, D Nguyen, Y Atchade, S Stoev and AA King., 2015, “Inference for dynamic and latent variable models via iterated, perturbed Bayes maps,” PNAS 112:719-724).