Taylor series expansions for the entropy rate of Hidden Markov Processes

Finding the entropy rate of Hidden Markov Processes is an active research topic, of both theoretical and practical importance. A recently used approach is studying the asymptotic behavior of the entropy rate in various regimes. In this paper we generalize and prove a previous conjecture relating the entropy rate to entropies of finite systems. Building on our new theorems, we establish series expansions for the entropy rate in two different regimes. We also study the radius of convergence of the two series expansions

Asymptotics of entropy rate in special families of hidden Markov chains

We derive an asymptotic formula for entropy rate of a hidden Markov chain under certain parameterizations. We also discuss applications of the asymptotic formula to the asymptotic behaviors of entropy rate of hidden Markov chains as outputs of certain channels, such as binary symmetric channel, binary erasure channel, and some special Gilbert-Elliot channel. © 2006 IEEE.published_or_final_versio

Analyticity of Entropy Rate of Hidden Markov Chains With Continuous Alphabet

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 chan- nel, 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 estab- lish the analyticity of the entropy rate for two representative channels: Cauchy and Gaussian.published_or_final_versio

Analyticity of Entropy Rates of Continuous-State Hidden Markov Models

The analyticity of the entropy and relative entropy rates of continuous-state hidden Markov models is studied here. Using the analytic continuation principle and the stability properties of the optimal filter, the analyticity of these rates is shown for analytically parameterized models. The obtained results hold under relatively mild conditions and cover several classes of hidden Markov models met in practice. These results are relevant for several (theoretically and practically) important problems arising in statistical inference, system identification and information theory

A Randomized Algorithm for the Capacity of Finite-State Channels

Inspired by ideas from the field of stochastic approximation, we propose a ran- domized algorithm to compute the capacity of a finite-state channel with a Markovian input. When the mutual information rate of the channel is concave with respect to the chosen parameterization, the proposed algorithm proves to be convergent to the ca- pacity of the channel almost surely with the derived convergence rate. We also discuss the convergence behavior of the algorithm without the concavity assumption.published_or_final_versio

Concavity of the mutual information rate for input-restricted memoryless channels at high SNR

We consider a memoryless channel with an input Markov process supported on a mixing finite-type constraint. We continue the development of asymptotics for the entropy rate of the output hidden Markov chain and deduce that, at high signal-to-noise ratio, the mutual information rate of such a channel is concave with respect to "almost" all input Markov chains of a given order. © 2012 IEEE.published_or_final_versio

Concavity of mutual information rate of finite-state channels

The computation of the capacity of a finite-state channel (FSC) is a fundamental and long-standing open problem in information theory. The capacity of a memoryless channel can be effectively computed via the classical Blahut-Arimoto algorithm (BAA), which, however, does not apply to a general FSC. Recently Vontobel et al. [1] generalized the BAA to compute the capacity of a finite-state machine channel with a Markovian input. Their proof of the convergence of this algorithm, however, depends on the concavity conjecture posed in their paper. In this paper, we confirm the concavity conjecture for some special FSCs. On the other hand, we give examples to show that the conjecture is not true in general.published_or_final_versio