We deal with zero-delay source coding of a vector-valued Gauss–Markov source subject to
a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay …

In this paper, we develop finite-time horizon causal filters for general processes taking
values in Polish spaces using the nonanticipative rate distortion function (NRDF) …

In this article, a modified mutual information maximization (InfoMax) framework, named
channel capacity maximization (CapMax), is proposed and applied to learn informative …

Directed information (DI) is a fundamental measure for the study and analysis of sequential
stochastic models. In particular, when optimized over input distributions it characterizes the …

We derive the finite-time horizon nonanticipative rate distortion function (NRDF) of
timevarying scalar Gauss-Markov sources under an average mean squared-error (MSE) …

Modelling causal relationships has become popular across various disciplines. Most
common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) …

The paper presents a methodology for identifying the topology of strictly causal dynamical
networks. Using a graph-theoretic representation of interconnected dynamical systems …

Directed information (DI) is a fundamental measure for the study and analysis of sequential
stochastic models. In particular, when optimized over the input distribution, it characterizes …

The existence of an optimal reverse-waterfilling algorithm to compute the nonanticipative
rate distortion function (NRDF) for time-invariant vector-valued Gauss–Markov processes …

We study the problem of characterizing and computing the Gaussian nonanticipative rate
distortion function (NRDF) of partially observable multivariate Gauss-Markov processes with …