Book chapter
Spatial Bayesian Nonparametric Methods
Nonparametric Bayesian Inference in Biostatistics, pp.347-357
Frontiers in Probability and the Statistical Sciences, Springer International Publishing
01/01/2015
DOI: 10.1007/978-3-319-19518-6_17
Abstract
We review nonparametric Bayesian approaches to inference for spatial data. The discussion is organized by increasing level of relaxation of traditional parametric assumptions. We start by considering nonparametric priors for covariance functions in a Gaussian process model. Next we allow for non-Gaussian marginal distributions by introducing Gaussian copulas. Finally, we go fully non-parametric and discuss Dirichlet process mixtures for the coefficients in a kernel convolution, Dirichlet process mixtures of Gaussian processes and spatial stick-breaking priors.
Details
- Title: Subtitle
- Spatial Bayesian Nonparametric Methods
- Creators
- Brian James Reich - Department of Statistics, North Carolina State University, Raleigh, USAMontserrat Fuentes - Department of Statistics, North Carolina State University, Raleigh, USA
- Resource Type
- Book chapter
- Publication Details
- Nonparametric Bayesian Inference in Biostatistics, pp.347-357
- Series
- Frontiers in Probability and the Statistical Sciences
- DOI
- 10.1007/978-3-319-19518-6_17
- Publisher
- Springer International Publishing; Cham
- Language
- English
- Date published
- 01/01/2015
- Academic Unit
- Statistics and Actuarial Science; President; Biostatistics
- Record Identifier
- 9984065886902771
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