Bayesian Treed Models

By Hugh A. Chipman, Edward I. George, Robert E. McCulloch. 2002

221 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Learning Certifiably Optimal Rule Lists (2017, methodology): “Some exploration of the search space is done by Bayesian decision tree methods [7, 8, 12] and Bayesian rule-based methods [25, 45].”
Scalable Bayesian Rule Lists (2016, background): “Bayesian tree models may also be able to be constructed using our setup, where one would mine rules and create a globally optimal tree (Dension, Mallick, & Smith, 1998; Chipman, George, & McCulloch, 2002, 2010).”
BART: Bayesian Additive Regression Trees (2008, methodology): “Trees, Bayesian CART (CGM98), and Bayesian treed regression (Chipman, George & McCulloch 2002) models were not considered, since they tend to sac-riﬁce predictive performance for interpretability.”
An interpretable model for stroke prediction using rules and Bayesian analysis (2014, methodology): “We extracted every patient in the MDCD database with a diagnosis of atrial fibrillation, one year of atrial fibrillation-free observation time prior to the diagnosis, and one year of observation time following the diagnosis (n=12,586).”
Fifty Years of Classification and Regression Trees (2014, methodology): “Chipman et al. (2002) extended the Bayesian approach of Chipman et al. (1998) to piecewise linear regression trees. Fan & Gray (2005) and Gray & Fan (2008) used a genetic algorithm for tree construction.”
Learning Accurate and Interpretable Classifiers Using Optimal Multi-Criteria Rules (2013, background): “Bayesian Decision Trees [Chipman et al. 2002] are also constructed in an approximate way, where the sampling procedure repeatedly restarts when the samples start to concentrate around a posterior mode, which is claimed to happen quickly.”
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling (2007, methodology): “Thus we introduce here an expansion of GPs, based on the idea of Bayesian partition models (Chipman et al., 2002; Denison et al., 2002), which is able to address these issues.”
Bayesian Treed Generalized Linear Models (2003, result): “The results in this paper extend the results of Chipman, George and McCulloch (1998, 2002) (hereafter CGM 1998 and CGM 2002) and Denison, Mallick and Smith (1998), (hereafter DMS 1998).”