Iterated Filtering

Details Iterated Filtering Iterated Filtering

2009-02-02

arxiv.org/abs/0902.0347v1

Mathematics

Statistics Theory

Scientific paper

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 that have been intractable via alternative methodologies.

Affiliated with

Also associated with

No associations

Rating

Iterated Filtering does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Iterated Filtering, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Iterated Filtering will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-160385