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Cooperative and graph signal processing
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SYSNO ASEP 0493396 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Bayesian approach to collaborative inference in networks of agents Author(s) Dedecius, Kamil (UTIA-B) RID, ORCID
Djurić, P. M. (US)Number of authors 2 Source Title Cooperative and graph signal processing. - London : Academic Press, 2018 / Djurić Petar M. ; Richard Cédric - ISBN 978-0-12-813677-5 Pages s. 131-145 Number of pages 15 s. Number of pages 837 Publication form Print - P Language eng - English Country US - United States Keywords Distributed estimation ; diffusion network ; information diffusion Subject RIV BC - Control Systems Theory OECD category Automation and control systems R&D Projects GA16-09848S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000488278200005 DOI 10.1016/B978-0-12-813677-5.00004-3 Annotation Bayesian inference has become a standard tool in the modern statistical signal processing theory, particularly due to the probabilistically consistent representation of the available knowledge about the variables of interest, and the amount of the uncertainty contained in this knowledge. Unlike in the 'standard' theory, the underlying inferential principles are generally applicable to virtually any inference task, from linear models to nonlinear, mixture, or hierarchical models. Furthermore, the rapid development of the modern devices with high computational performance finally eliminated the major drawback of the Bayesian theory: the frequent analytical intractability of the posterior distributions. This chapter studies the possible implementation of the Bayesian inference in networks of collaborating agents. In particular, we focus on the diffusion networks, where the agents may share information (measurements and/or estimates) with their adjacent neighbors, and incorporate it into own knowledge about the unknown variables of interest. There are several ways how to perform this incorporation in an optimal way according to a convenient user-selected information criterion, and under certain conditions where the model belongs to the exponential family of distributions and the prior distributions are conjugate, the results are analytically tractable. The celebrated Kalman filter serves as an illustrative example demonstrating the straightforward application of the abstractly described principles to a particular problem. It is reformulated for the collaborative estimation task in networks where both the neighbors' observations and posterior distributions are available to each agent. Naturally, the analyticity of the resulting filter is preserved. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2019
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