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- 216 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Gaussian Process Regression Analysis for Functional Data
Book details
Table of contents
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About This Book
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Coveri
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Yes, you can access Gaussian Process Regression Analysis for Functional Data by Jian Qing Shi, Taeryon Choi in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Front Cover
- Contents
- List of Figures
- List of Tables
- Preface
- List of Abbreviations and Symbols
- 1. Introduction
- 2. Bayesian nonlinear regression with Gaussian process priors
- 3. Inference and computation for Gaussian process regression (GPR) model
- 4. Covariance function and model selection
- 5. Functional regression analysis
- 6. Mixture models and curve clustering
- 7. Generalized Gaussian process regression for non-Gaussian functional data
- 8. Some other related models
- Appendix
- Bibliography