6533b821fe1ef96bd127c518

RESEARCH PRODUCT

Contributed discussion on article by Pratola

Oksana ChkrebtiiLuca MartinoJeffrey S. RosenthalRadu V. CraiuMaurits KapteinScotland LemanFrancisco LouzadaReihaneh EntezariAndrew HoeghRafael Bassi SternAbdolreza Mohammadi

subject

Statistics and Probabilitymodel selectionMarkov Chain Monte Carlo (MCMC)Bayesian regression treeComputer scienceBig dataBayesian regression tree (BRT) modelsComputingMilieux_LEGALASPECTSOFCOMPUTINGbirth–death processMachine learningcomputer.software_genreSequential Monte Carlo methods01 natural sciencespopulation Markov chain Monte Carlo010104 statistics & probabilitysymbols.namesakebig data0502 economics and businessBayesian Regression Trees (BART)0101 mathematics050205 econometrics Bayesian treed regressionMultiple Try Metropolis algorithmsINFERÊNCIA ESTATÍSTICAbusiness.industryApplied MathematicsModel selection05 social sciencesRejection samplingData scienceVariable-order Bayesian networkTree (data structure)Tree traversalMarkov chain Monte Carlocontinuous time Markov processsymbolsArtificial intelligencebusinessBayesian linear regressioncommunication-freecomputerGibbs sampling

description

The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.

yearjournalcountryeditionlanguage
2016-09-01Bayesian Analysis
10.1214/16-ba999hhttps://doi.org/10.1214/16-BA999H