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This chapter introduces the Bayesian framework and how it can be applied to the various areas of quantitative finance, including derivative modeling, banking, financial management, insurance, and investments. We highlight the impact machine learning has had on the finance industry and the potential benefits of framing problems in quantitative finance within the Bayesian framework. The Bayesian framework allows us to naturally answer questions such as: (1) How can we explain the prediction or output of the models? (2) What is the distribution of the parameters of the model? (3) How do we select between the different models in a statistically principled manner? and (4) Which inputs are most relevant for the task at hand? This book aims to answer these questions across various fields within quantitative finance.

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Introduction to Bayesian Machine Learning in Quantitative Finance

  • 章节

  • 发表日期：

    2025年

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  • Wilson Tsakane Mongwe,
  • Rendani Mbuvha,
  • Tshilidzi Marwala

摘要

This chapter introduces the Bayesian framework and how it can be applied to the various areas of quantitative finance, including derivative modeling, banking, financial management, insurance, and investments. We highlight the impact machine learning has had on the finance industry and the potential benefits of framing problems in quantitative finance within the Bayesian framework. The Bayesian framework allows us to naturally answer questions such as: (1) How can we explain the prediction or output of the models? (2) What is the distribution of the parameters of the model? (3) How do we select between the different models in a statistically principled manner? and (4) Which inputs are most relevant for the task at hand? This book aims to answer these questions across various fields within quantitative finance.