Iterated filtering
Edward L. Ionides, Anindya Bhadra, Yves Atchad\'e, Aaron King

TL;DR
This paper develops new theoretical results on the convergence of iterated filtering algorithms used for inference in partially observed Markov process models, supported by empirical evidence of their effectiveness.
Contribution
It introduces a new recursive approach for likelihood maximization via importance sampling and establishes convergence theory for iterated filtering algorithms.
Findings
Convergence of iterated filtering algorithms is theoretically supported.
A new recursive likelihood maximization method is proposed.
Iterated importance sampling is introduced as a special case.
Abstract
Inference for partially observed Markov process models has been a longstanding methodological 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 provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
