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Approximate inference in hidden Markov models using iterative active state selection

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)65 - 68
Number of pages4
JournalIEEE Signal Processing Letters
Issue number2
DatePublished - Feb 2006


The inferential task of computing the marginal posterior probability mass functions of state variables and pairs of consecutive state variables of a hidden Markov model is considered. This can be exactly and efficiently performed using a message passing scheme such as the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. We present a novel iterative reduced complexity variation of the BCJR algorithm that uses reduced support approximations for the forward and backward messages, as in the M-BCJR algorithm. Forward/backward message computation is based on the concept of expectation propagation, which results in an algorithm similar to the M-BCJR algorithm with the active state selection criterion being changed from the filtered distribution of state variables to beliefs of state variables. By allowing possibly different supports for the forward and backward messages, we derive identical forward and backward recursions that can be iterated. Simulation results of application for trellis-based equalization of a wireless communication system confirm the improved performance over the M-BCJR algorithm

Additional information

Publisher: Institute of Electrical and Electronics Engineers, Inc. (IEEE) Rose publication type: Journal article Terms of use: Copyright © 2005 IEEE. Reprinted from IEEE Signal Processing Letters. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it

    Research areas

  • deterministic algorithms, equalizers, hidden Markov models (HMMs), message passing, state space methods

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