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Scaling Posterior Distributions over Differently-Curated Datasets: A Bayesian-Neural-Networks Methodology

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Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

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Abstract

This paper provides an introduction to an innovative methodology for scaling posterior distributions over differently-curated datasets. The proposed methodology is based on Bayesian Neural Networks, improved by effective sampling algorithms. These algorithms finally realize a suitable model setup for improving the scaling effect. Theoretical results are presented and discussed in details, as well as a modern case study focused on stock quotation prediction that confirms the successful application of our proposed methodology to emerging big data analytics settings.

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Author information

Authors and Affiliations

  1. iDEA Lab, University of Calabria, Rende, Italy

    Alfredo Cuzzocrea & Selim Soufargi

  2. ISIRES, Turin, Italy

    Alessandro Baldo & Edoardo Fadda

  3. DISMA, Politecnico di Torino, Turin, Italy

    Edoardo Fadda

Authors
  1. Alfredo Cuzzocrea
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  2. Selim Soufargi
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  3. Alessandro Baldo
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  4. Edoardo Fadda
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università della Calabria, Rende, Italy

    Sergio Flesca

  3. Università Federico II di Napoli, Naples, Italy

    Elio Masciari

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Cuzzocrea, A., Soufargi, S., Baldo, A., Fadda, E. (2022). Scaling Posterior Distributions over Differently-Curated Datasets: A Bayesian-Neural-Networks Methodology. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-16564-1_19

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  • Online ISBN: 978-3-031-16564-1

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