Bayesian Regression and Gaussian Processes

Autor:	Matthew Dixon, Igor Halperin, Paul Bilokon
Rok vydání:	2020
Předmět:	business.industry Computer science Bayesian probability Machine learning computer.software_genre symbols.namesake Kriging Frequentist inference symbols Artificial intelligence business Bayesian linear regression Gaussian process computer Intuition
Zdroj:	Machine Learning in Finance ISBN: 9783030410674
DOI:	10.1007/978-3-030-41068-1_3
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6960850ab11bda59570834cacfec3503 https://doi.org/10.1007/978-3-030-41068-1_3 Zobrazit plný text záznamu