Embedding black-box regression techniques into hierarchical Bayesian models

DOI10.1080/00949655.2011.594052zbMath1431.62170OpenAlexW1987780449MaRDI QIDQ4925453

Daniel Fink, Benjamin A. Shaby

Publication date: 12 June 2013

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2011.594052

zbMATH Keywords

hierarchical BayesianMarkov chain Monte Carlodata miningmachine learning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• rpart
• gbm

Cites Work

• Unnamed Item
• Predictive learning via rule ensembles
• Greedy function approximation: A gradient boosting machine.
• Nonparametric Bayesian data analysis
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Fast Stable Direct Fitting and Smoothness Selection for Generalized Additive Models
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Model-Based Geostatistics
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Monte Carlo sampling methods using Markov chains and their applications
• Random forests
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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