An overview of statistical and information-theoretic aspects of hidden Markov processes
(HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain …

Here is a work that adds much to the sum of our knowledge in a key area of science today. It
is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov …

Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic
processes that arise in many applied fields including communication, target-tracking, and …

An autoregressive process with Markov regime is an autoregressive process for which the
regression function at each time point is given by a nonobservable Markov chain. In this …

In this paper, we introduce a general framework for treating channels with memory and
feedback. First, we prove a general feedback channel coding theorem based on Massey's …

Four estimators of the directed information rate between a pair of jointly stationary ergodic
finite-alphabet processes are proposed, based on universal probability assignments. The …

We study the stability of the optimal filter wrt its initial condition and wrt the model for the
hidden state and the observations in a general hidden Markov model, using the Hilbert …

Consider a parametrized family of general hidden Markov models, where both the observed
and unobserved components take values in a complete separable metric space. We prove …

We consider the optimal sensor scheduling problem formulated as a partially observed
Markov decision process (POMDP). Due to operational constraints, at each time instant, the …

The paper develops a simulation-based approach to sequential parameter learning and
filtering in general state space models. Our approach is based on approximating the target …