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Covariance Functions.pdf

RW4.pdf
Preview of Covariance Functions
🔗 Source: gaussianprocess.org
📊 Size: 675 KB
👤 Author: Carl Edward Rasmusen and Christopher K. I. Williams
⬇️ Downloads: 139

Summary

Chapter 4 of "Gaussian Processes for Machine Learning" discusses covariance functions, which encode assumptions about the function to be learned and define similarity between data points. A valid covariance function must be positive semidefinite. The chapter covers stationary, dot-product, and non-stationary covariance functions, as well as methods for constructing new ones. It also introduces eigenfunction analysis and Mercer's theorem, which allows covariance functions to be expressed in terms of eigenfunctions and eigenvalues under certain conditions. Additionally, the chapter describes how to define covariance functions for input domains over structured objects like strings and trees. Key concepts include stationarity, isotropy, and positive semidefiniteness, as well as the relationship between covariance functions and kernel functions.

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Description

Chapter 4 of "Gaussian Processes for Machine Learning" discusses covariance functions, which encode assumptions about the function to be learned and define...

Technical Information

  • File Format: PDF
  • File Size: 675 KB
  • Pages: 26
  • Language: EN
  • Author: Carl Edward Rasmusen and Christopher K. I. Williams
  • Total Downloads: 139
  • Last Updated: 2 weeks ago

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This PDF document about Covariance Functions provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Covariance Functions.

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