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Maximum Likelihood Sequence Estimation for Hidden Reciprocal Processes
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DOI：10.1109/TAC.2013.2256012.png)
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This paper addresses the problem of maximum likelihood sequence estimation (MLSE) based on a hidden reciprocal chain (HRC) as the underlying target model. HRCs are non-causal, discrete-time finite-state stochastic processes which can be regarded as the one-dimensional version of a Markov random field, although they are not in general Markov processes. This paper describes a procedure for evaluating the MLSE for a HRC and compares the resultant estimator with its Markov Model equivalent: the Viterbi algorithm. In addition, the performance of the newly proposed reciprocal MLSE is compared to a HRC-based optimal smoother.
Keyword:
Hidden Markov models
Markov random fields
maximum likelihood estimation
state estimation
stochastic systems
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7
论文数:
1.3W
被引数:
6.7W
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