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Bayesian Multimodal Data Analytics: AnIntroduction

Author

Listed:
  • Marco Luigi Giuseppe Grasso

    (Politecnico di Milano)

  • Panagiotis Tsiamyrtzis

    (Politecnico di Milano
    Athens University of Economics and Business)

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.

Suggested Citation

  • Marco Luigi Giuseppe Grasso & Panagiotis Tsiamyrtzis, 2024. "Bayesian Multimodal Data Analytics: AnIntroduction," Springer Optimization and Its Applications,, Springer.
  • Handle: RePEc:spr:spochp:978-3-031-53092-0_16
    DOI: 10.1007/978-3-031-53092-0_16
    as

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