Bayesian CART Model Search

By Hugh A. Chipman, Edward I. George, Robert E. McCulloch. 1998

Subfields: Probabilistic & Bayesian Methods, Statistical Learning Theory

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Citations in the corpus

Learning Certifiably Optimal Rule Lists (2017, methodology): “Recent approaches use Bayesian analysis, either to nd a locally optimal solution [6] or to explore the search space [25, 45].”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “Bayesian tree methods [14,72] find local posterior modes using top-down greedy methods like regular decision trees.”
Falling Rule Lists (2014, background): “(Even Bayesian tree methods that aim to traverse a wider search space use greedy splitting and mostly produce local solutions, e.g., Chipman et al. , 1998, .)”
BART: Bayesian Additive Regression Trees (2008, methodology): “An early version of our work on BART (Chipman, George & McCulloch 2007) was published in the proceedings of the conference Advances in Neural Information Processing Systems 2006.”
A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates (2012, methodology): “The modified data can be used for other purposes, for example large scale hypothesis testing for determining which of a set of covariates interact with a treatment variable.”
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (2017, unknown): “Noting its strong performance in prediction tasks, Hill (2011) advocates the use of the Bayesian additive regression tree (BART) model of Chipman et al. (2010) for estimating these conditional expectations.”
Optimal constraint-based decision tree induction from itemset lattices (2010, methodology): “In (Buntine, 1992; Chipman et al., 1998; Angelopoulos & Cussens, 2005) the prior only allows for paths that contain at least a minimum number of examples, which corresponds to the anti-monotonic, structure independent minimum support constraint.”
Efficient sequential decision‐making algorithms for container inspection operations (2011, background): “[6,7] and Miglio and Soffritti [23] provide a comparison of various definitions of neighborhood and proximity between trees.”