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This book explores the utility of employing the Bayesian inference framework to solve various problems in quantitative finance. With the increase in data-driven and machine learning technologies that can be used to solve finance problems, we show that the Bayesian inference framework can be reliably used to 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? We apply this framework to problems in derivative pricing and modeling, banking, financial management, insurance, and investments. This chapter summarizes the insights we obtained from the themes covered by the book, as well as ongoing and future research directions.

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Conclusions 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 book explores the utility of employing the Bayesian inference framework to solve various problems in quantitative finance. With the increase in data-driven and machine learning technologies that can be used to solve finance problems, we show that the Bayesian inference framework can be reliably used to 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? We apply this framework to problems in derivative pricing and modeling, banking, financial management, insurance, and investments. This chapter summarizes the insights we obtained from the themes covered by the book, as well as ongoing and future research directions.