Spiked Dirichlet process priors for Gaussian process models
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Publication:544464
DOI10.1155/2010/201489zbMath1214.62100OpenAlexW1977964948WikidataQ42552461 ScholiaQ42552461MaRDI QIDQ544464
Terrance D. Savitsky, Marina Vannucci
Publication date: 15 June 2011
Published in: Journal of Probability and Statistics (Search for Journal in Brave)
Full work available at URL: https://eudml.org/doc/231329
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Uses Software
Cites Work
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- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Variable selection for nonparametric Gaussian process priors: Models and computational strategies
- Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
- Ferguson distributions via Polya urn schemes
- A predictive view of Bayesian clustering
- A Bayesian analysis of some nonparametric problems
- Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage
- Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes
- Estimating Optimal Transformations for Multiple Regression and Correlation
- Multivariate Bayesian Variable Selection and Prediction
- Bayesian Density Estimation and Inference Using Mixtures
- Detecting differential gene expression with a semiparametric hierarchical mixture method
- Spiked Dirichlet process prior for Bayesian multiple hypothesis testing in random effects models
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