A stopping rule for symbolic dynamic filtering

Yicheng Wen; Asok Ray

doi:10.1016/j.aml.2010.04.048

A stopping rule for symbolic dynamic filtering

Yicheng Wen
, Asok Ray

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

One of the key issues in symbolic dynamic filtering (SDF) is how to obtain a lower bound on the length of symbol blocks for computing the state probability vectors of probabilistic finite-state automata (PFSA). Having specified an absolute error bound at a confidence level, this short work formulates a stopping rule by making use of Markov chain Monte Carlo (MCMC) computations.

Original language	English (US)
Pages (from-to)	1125-1128
Number of pages	4
Journal	Applied Mathematics Letters
Volume	23
Issue number	9
DOIs	https://doi.org/10.1016/j.aml.2010.04.048
State	Published - Sep 2010

All Science Journal Classification (ASJC) codes

Applied Mathematics

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10.1016/j.aml.2010.04.048

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abstract = "One of the key issues in symbolic dynamic filtering (SDF) is how to obtain a lower bound on the length of symbol blocks for computing the state probability vectors of probabilistic finite-state automata (PFSA). Having specified an absolute error bound at a confidence level, this short work formulates a stopping rule by making use of Markov chain Monte Carlo (MCMC) computations.",

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AB - One of the key issues in symbolic dynamic filtering (SDF) is how to obtain a lower bound on the length of symbol blocks for computing the state probability vectors of probabilistic finite-state automata (PFSA). Having specified an absolute error bound at a confidence level, this short work formulates a stopping rule by making use of Markov chain Monte Carlo (MCMC) computations.

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A stopping rule for symbolic dynamic filtering

Abstract

All Science Journal Classification (ASJC) codes

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