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BART (Bayesian Additive Regression Trees) has become increasingly popular as a flexible and scalable nonparametric regression approach for modern applied statistics problems.
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“High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis.”
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“A full scale approximation of covariance functions for large spatial data sets.”
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“A Generalized Least-Square Matrix Decomposition.”
Allen, G., Grosenick, L., and Taylor, J. (2013) · 2013
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“Bayesian Additive Regression Trees With Parametric Models of Heteroskedasticity.”
Bleich, J. and Kapelner, A. (2014) · 2014
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“Efficient Metropolis-Hastings Proposal Mechanisms for Bayesian Regression Tree Models.”
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“Bayesian Treed Gaussian Process Models with an Application to Computer Modeling.”
Gramacy, R. and Lee, H. (2008) · 2008
Cited alongside, same era.
“BART: Bayesian additive regression trees.”
— (2010) · 2010
Cited alongside, same era.
Pratola, M. T. (2016) · 2016
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“Log-Linear Bayesian Additive Regression Trees for Categorical and Count Responses.”
Murray, J. S. (2017) · 2017
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“Posterior Concentration for Bayesian Regression Trees and Their Ensembles.”
Rockova, V. and van der Pas, S. (2017) · 2017
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