Background to Bayesian Machine Learning in Quantitative Finance

Wilson Tsakane Mongwe (), Rendani Mbuvha () and Tshilidzi Marwala
Additional contact information
Wilson Tsakane Mongwe: University of Johannesburg
Rendani Mbuvha: University of Witwatersrand
Tshilidzi Marwala: United Nations University

Chapter Chapter 2 in Bayesian Machine Learning in Quantitative Finance, 2025, pp 13-30 from Springer

Abstract: 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.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-88431-3_2

Ordering information: This item can be ordered from
http://www.springer.com/9783031884313

DOI: 10.1007/978-3-031-88431-3_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().