Bayesian Regression and Gaussian Processes

Matthew F. Dixon, Igor Halperin and Paul Bilokon
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Matthew F. Dixon: Illinois Institute of Technology, Department of Applied Mathematics
Igor Halperin: New York University, Tandon School of Engineering
Paul Bilokon: Imperial College London, Department of Mathematics

Chapter Chapter 3 in Machine Learning in Finance, 2020, pp 81-109 from Springer

Abstract: Abstract This chapter introduces Bayesian regression and shows how it extends many of the concepts in the previous chapter. We develop kernel based machine learning methods—specifically Gaussian process regression, an important class of Bayesian machine learning methods—and demonstrate their application to “surrogate” models of derivative prices. This chapter also provides a natural starting point from which to develop intuition for the role and functional form of regularization in a frequentist setting—the subject of subsequent chapters.

Date: 2020
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DOI: 10.1007/978-3-030-41068-1_3

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