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This chapter explores statistical learning, spotlighting the role of Gaussian process (GP) models. It initiates with a foundational exposition on data-driven curve fitting, focusing on probabilistic modeling via GP regression. We especially highlight the role of GP kernels and GP mean functions on the fit. The chapter provides two extended case studies rooted in actuarial applications: mortality rate modeling and variable annuity valuation. Analysis is illustrated with a plenty of figures and results, and the chapter is supplemented by a companion Github repository so that users can get hands-on engagement through the provided Python and R notebooks. The outlined research projects serve as conduits for students to deepen their understanding and better navigate the multifaceted aspects of the actuarial applications.

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Gaussian Processes for Statistical Learning in Actuarial Science

  • 章节

  • 发表日期：

    2026年

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  • Mike Ludkovski,
  • Jimmy Risk

摘要

This chapter explores statistical learning, spotlighting the role of Gaussian process (GP) models. It initiates with a foundational exposition on data-driven curve fitting, focusing on probabilistic modeling via GP regression. We especially highlight the role of GP kernels and GP mean functions on the fit. The chapter provides two extended case studies rooted in actuarial applications: mortality rate modeling and variable annuity valuation. Analysis is illustrated with a plenty of figures and results, and the chapter is supplemented by a companion Github repository so that users can get hands-on engagement through the provided Python and R notebooks. The outlined research projects serve as conduits for students to deepen their understanding and better navigate the multifaceted aspects of the actuarial applications.