Inference for dynamic and latent variable models via iterated, perturbed Bayes maps.

Abstract

Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood… (More)
DOI: 10.1073/pnas.1410597112

Topics

2 Figures and Tables

Statistics

0100200300201520162017
Citations per Year

Citation Velocity: 55

Averaging 55 citations per year over the last 3 years.

Learn more about how we calculate this metric in our FAQ.

Cite this paper

@article{Ionides2015InferenceFD, title={Inference for dynamic and latent variable models via iterated, perturbed Bayes maps.}, author={Edward L. Ionides and Dao Nguyen and Y. F. Atchad{\'e} and Stilian Stoev and Aaron A. King}, journal={Proceedings of the National Academy of Sciences of the United States of America}, year={2015}, volume={112 3}, pages={719-24} }