Bayesian Multimodal Data Analytics: AnIntroduction

Marco Luigi Giuseppe Grasso () and Panagiotis Tsiamyrtzis ()
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Marco Luigi Giuseppe Grasso: Politecnico di Milano
Panagiotis Tsiamyrtzis: Politecnico di Milano

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 347-355 from Springer

Abstract: Abstract Bayesian methods for multimodal data have attracted the interest of researchers and practitioners in a variety of real-world applications. Indeed, Bayesian statistics provides an effective framework to deal with mixtures of unimodal distributions, allowing one to incorporate prior information when available and to model posterior distributions in distinct modes. This introductory chapter presents a brief overview of the Bayesian perspective in the field of multimodal data, as well as a brief overview of salient applications. This chapter additionally offers the reader an introduction to two subsequent studies, wherein Bayesian modeling methods are presented for addressing multimodal data in the context of risk analysis and gestural human–machine interaction problems, respectively.

Keywords: Bayesian statistics; Prior distribution; Posterior distribution; Industrial quality; Risk analysis; Human–machine interaction. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-53092-0_16

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DOI: 10.1007/978-3-031-53092-0_16

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