Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy

2022-02-20 · Antonio R. Linero

Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary \emph{generalized BART} models by introducing a very simple, tuning-parameter-free, reversible jump Markov chain Monte Carlo algorithm. Our algorithm requires only that the user be able to compute the likelihood and (optionally) its gradient and Fisher information. The potential applications are very broad; we consider examples in survival analysis, structured heteroskedastic regression, and gamma shape regression.

📄 PDF Abstract BibTeX arXiv:2202.09924

Code (0)

등록된 구현이 없습니다.

Tasks

regressionSurvival AnalysisUncertainty Quantification

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Residual Connection 설명 없음

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Refunds@Expedia|||How do I get a full refund from Expedia? “How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Quick Help &…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Theory of Posterior Concentration for Generalized Bayesian Additive Regression Trees

2023-04-25 · Enakshi Saha

Bayesian Additive Regression Trees (BART) are a powerful semiparametric ensemble learning technique for modeling nonlinear regression functions. Although initially BART was proposed for predicting only continuous and bin…

Ensemble Learning regression

MPBART - Multinomial Probit Bayesian Additive Regression Trees

2013-09-30 · Bereket P. Kindo, Hao Wang, Edsel A. Peña

This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow incl…

General Classification regression

Bayesian quantile additive regression trees

2016-07-10 · Bereket P. Kindo, Hao Wang, Timothy Hanson, Edsel A. Peña

Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much atten…

Binary Classification General Classification quantile regression regression

Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression

2022-11-14 · Eoghan O'Neill

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Addi…

regression Vocal Bursts Type Prediction

Particle Gibbs for Bayesian Additive Regression Trees

2015-02-16 · Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

Additive regression trees are flexible non-parametric models and popular off-the-shelf tools for real-world non-linear regression. In application domains, such as bioinformatics, where there is also demand for probabilis…

regression