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Nonanticipative Rate Distortion Function and Filtering Theory: A weak Convergence Approach

2012/12/29 by Photios A. Stavrou, Stavrou, Photios A., Charalambos D. Charalambous +1
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Systems and Control (eess.SY) #Wireless Communication Security Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1212.6643

openalex publication_date 2012/12/29 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

In this paper the relation between nonanticipative rate distortion function (RDF) and Bayesian filtering theory is further investigated on general Polish spaces. The relation is established via an optimization on the space of conditional distributions of the so-called directed information subject to fidelity constraints. Existence of the optimal reproduction distribution of the nonanticipative RDF is shown using the topology of weak convergence of probability measures. Subsequently, we use the solution of the nonanticipative RDF to present the realization of a multidimensional partially observable source over a scalar Gaussian channel. We show that linear encoders are optimal, establishing joint source-channel coding in real-time.

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