The feature on the posterior conditional probability of finite state Markov channel

The feature on the posterior conditional probability of finite state Markov channel

The feature of finite state Markov channel probability distribution is discussed on condition that original I/O are known. The probability is called posterior condition probability. It is also proved by Bayes formula that posterior condition probability forms stationary Markov sequence if channel in...

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Veröffentlicht in:Journal of Harbin Institute of Technology 2005-08, Vol.12 (4), p.446-449
1. Verfasser: 母丽华 沈继红 苑延华
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Markov过程
Markov链
有限状态
概率分布
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description The feature of finite state Markov channel probability distribution is discussed on condition that original I/O are known. The probability is called posterior condition probability. It is also proved by Bayes formula that posterior condition probability forms stationary Markov sequence if channel input is independently and identically distributed. On the contrary, Markov property of posterior condition probability isn＇ t kept if the input isn＇t independently and identically distributed and a numerical example is utilized to explain this case. The properties of posterior condition probability will aid the study of the numerical calculated recurrence formula of finite state Markov channel capacity.
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subjects Markov过程
Markov链
有限状态
概率分布
title The feature on the posterior conditional probability of finite state Markov channel
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