Uncertainty in gradient boosting via ensembles
For many practical, high-risk applications, it is essential to quantify uncertainty in a model's
predictions to avoid costly mistakes. While predictive uncertainty is widely studied for neural …
predictions to avoid costly mistakes. While predictive uncertainty is widely studied for neural …
The how and why of Bayesian nonparametric causal inference
Spurred on by recent successes in causal inference competitions, Bayesian nonparametric
(and high‐dimensional) methods have recently seen increased attention in the causal …
(and high‐dimensional) methods have recently seen increased attention in the causal …
Barp: Improving mister p using bayesian additive regression trees
J Bisbee - American Political Science Review, 2019 - cambridge.org
Multilevel regression and post-stratification (MRP) is the current gold standard for
extrapolating opinion data from nationally representative surveys to smaller geographic …
extrapolating opinion data from nationally representative surveys to smaller geographic …
Mediation analysis using Bayesian tree ensembles.
We present a general framework for causal mediation analysis using nonparametric
Bayesian methods in the potential outcomes framework. Our model, which we refer to as the …
Bayesian methods in the potential outcomes framework. Our model, which we refer to as the …
Bayesian survival tree ensembles with submodel shrinkage
Bayesian Survival Tree Ensembles with Submodel Shrinkage Page 1 Bayesian Analysis (2022)
17, Number 3, pp. 997–1020 Bayesian Survival Tree Ensembles with Submodel Shrinkage …
17, Number 3, pp. 997–1020 Bayesian Survival Tree Ensembles with Submodel Shrinkage …
Semiparametric mixed‐scale models using shared Bayesian forests
This paper demonstrates the advantages of sharing information about unknown features of
covariates across multiple model components in various nonparametric regression …
covariates across multiple model components in various nonparametric regression …
Stochastic tree ensembles for regularized nonlinear regression
This article develops a novel stochastic tree ensemble method for nonlinear regression,
referred to as accelerated Bayesian additive regression trees, or XBART. By combining …
referred to as accelerated Bayesian additive regression trees, or XBART. By combining …
Bayesian additive regression trees with model trees
Bayesian additive regression trees (BART) is a tree-based machine learning method that
has been successfully applied to regression and classification problems. BART assumes …
has been successfully applied to regression and classification problems. BART assumes …
Semiparametric analysis of clustered interval‐censored survival data using soft Bayesian additive regression trees (SBART)
Popular parametric and semiparametric hazards regression models for clustered survival
data are inappropriate and inadequate when the unknown effects of different covariates and …
data are inappropriate and inadequate when the unknown effects of different covariates and …
Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components
Bayesian additive regression tree (BART) is a recent statistical method that combines
ensemble learning and nonparametric regression. BART is constructed under a probabilistic …
ensemble learning and nonparametric regression. BART is constructed under a probabilistic …