Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo

Maria Prosperina Vitale (), Giuseppe Giordano and Giancarlo Ragozini
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Maria Prosperina Vitale: University of Salerno
Giuseppe Giordano: University of Salerno
Giancarlo Ragozini: University of Naples Federico II

Statistical Methods & Applications, 2022, vol. 31, issue 2, No 8, 269-278

Abstract: Abstract In the present contribution we provide a discussion of the paper on “Bayesian graphical models for modern biological applications”. The authors present an extensive review of Bayesian graphical models, which are used for a variety of inferential tasks applied to biology and medicine settings. Our contribution proposes a conceptual connection between two scientific frameworks, graphical models and social network analysis, by highlighting also the role played by network models and random graphs. A bibliometric analysis is performed by exploiting publications collected from online bibliographic archives to map the main themes characterizing the two research fields. Specifically, a co-word network analysis is carried out using visualization tools and thematic evolution maps.

Keywords: Bibliometric analysis; Co-word analysis; Graph theory; Graphical model; Network model; Social network analysis; MSC code1; MSC code2; More (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10260-021-00603-4

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