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Background to Bayesian Machine Learning in Quantitative Finance

In: Bayesian Machine Learning in Quantitative Finance

Author

Listed:
  • Wilson Tsakane Mongwe

    (University of Johannesburg)

  • Rendani Mbuvha

    (University of Witwatersrand)

  • Tshilidzi Marwala

    (United Nations University)

Abstract

The Bayesian framework provides a probabilistically sound tool for solving various problems in the quantitative finance domain. This chapter discusses the Bayesian paradigm and how this translates to Bayesian machine learning. We discuss some of the advantages and disadvantages of the Bayesian framework and how Bayesian models can be trained and deployed. We also discuss the various performance metrics that can be used to assess the performance of Bayesian machine learning models.

Suggested Citation

  • Wilson Tsakane Mongwe & Rendani Mbuvha & Tshilidzi Marwala, 2025. "Background to Bayesian Machine Learning in Quantitative Finance," Springer Books, in: Bayesian Machine Learning in Quantitative Finance, chapter 0, pages 13-30, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-88431-3_2
    DOI: 10.1007/978-3-031-88431-3_2
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