Title :
Analyticity of Entropy Rate of Hidden Markov Chains With Continuous Alphabet
Author :
Guangyue Han ; Marcus, Brian
Author_Institution :
Dept. of Math., Univ. of Hong Kong, Hong Kong, China
Abstract :
We first prove that under certain mild assumptions, the entropy rate of a hidden Markov chain, observed when passing a finite-state stationary Markov chain through a discrete-time continuous-output channel, is analytic with respect to the input Markov chain parameters. We then further prove, under strengthened assumptions on the channel, that the entropy rate is jointly analytic as a function of both the input Markov chain parameters and the channel parameters. In particular, the main theorems establish the analyticity of the entropy rate for two representative channels: 1) Cauchy and 2) Gaussian. The analyticity results obtained are expected to be helpful in computation/estimation of entropy rate of hidden Markov chains and capacity of finite-state channels with continuous output alphabet.
Keywords :
Gaussian channels; channel capacity; channel estimation; entropy; estimation theory; hidden Markov models; Cauchy channel parameter; Gaussian channel parameter; continuous output alphabet; discretetime continuous-output channel; entropy rate analyticity; finite-state channel; finite-state stationary Markov chain; hidden Markov chain; input Markov chain parameter; Additives; Entropy; Hidden Markov models; Markov processes; Measurement; Zinc; Hidden Markov chain; Hilbert metric; analyticity; continuous alphabet; entropy rate; hidden Markov chain;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2015.2423558