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Summary: Iterated Filtering
Edward L. Ionides13
, Anindya Bhadra1
, Yves Atchad´e1
and Aaron King23
March 3, 2011
Departments of 1
Statistics and 2
Ecology and Evolutionary Biology,
The University of Michigan, Ann Arbor, Michigan, USA.
3
Fogarty International Center, National Institutes of Health.
email:[ionides,tatar,yvesa,kingaa]@umich.edu
Abstract
Inference for partially observed Markov process models has been a longstanding methodolog-
ical challenge with many scientific and engineering applications. Iterated filtering algorithms
maximize the likelihood function for partially observed Markov process models by solving a
recursive sequence of filtering problems. We present new theoretical results pertaining to the
convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This
theory complements the growing body of empirical evidence that iterated filtering algorithms
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