Efficient Metropolis-Hastings proposal mechanisms for Bayesian regression tree models comment

A. Mohammadi, M.C. Kaptein

Research output: Contribution to journalEditorialScientificpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)938-940
JournalBayesian Analysis
Volume11
Issue number3
Publication statusPublished - Sep 2016

Keywords

  • Markov chain Monte Carlo
  • birth-death process
  • continuous time Markov process
  • Bayesian regression tree
  • REVERSIBLE JUMP
  • MIXTURE

Cite this

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title = "Efficient Metropolis-Hastings proposal mechanisms for Bayesian regression tree models comment",
abstract = "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.",
keywords = "Markov chain Monte Carlo, birth-death process, continuous time Markov process, Bayesian regression tree, REVERSIBLE JUMP, MIXTURE",
author = "A. Mohammadi and M.C. Kaptein",
year = "2016",
month = "9",
language = "English",
volume = "11",
pages = "938--940",
journal = "Bayesian Analysis",
issn = "1936-0975",
publisher = "INT SOC BAYESIAN ANALYSIS",
number = "3",

}

Efficient Metropolis-Hastings proposal mechanisms for Bayesian regression tree models comment. / Mohammadi, A.; Kaptein, M.C.

In: Bayesian Analysis, Vol. 11, No. 3, 09.2016, p. 938-940.

Research output: Contribution to journalEditorialScientificpeer-review

TY - JOUR

T1 - Efficient Metropolis-Hastings proposal mechanisms for Bayesian regression tree models comment

AU - Mohammadi, A.

AU - Kaptein, M.C.

PY - 2016/9

Y1 - 2016/9

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AB - 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.

KW - Markov chain Monte Carlo

KW - birth-death process

KW - continuous time Markov process

KW - Bayesian regression tree

KW - REVERSIBLE JUMP

KW - MIXTURE

M3 - Editorial

VL - 11

SP - 938

EP - 940

JO - Bayesian Analysis

JF - Bayesian Analysis

SN - 1936-0975

IS - 3

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