Bayesian evaluation of informative hypotheses for multiple populations

Herbert Hoijtink*, Xin Gu, Joris Mulder

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple‐population data can properly be processed. The resulting multiple‐population approximate adjusted fractional Bayes factor is implemented in the R package Bain.
Original languageEnglish
Pages (from-to)219-243
JournalBritish Journal of Mathematical and Statistical Psychology
Volume72
Issue number2
DOIs
Publication statusPublished - 2019

Fingerprint

Fractional Bayes Factor
Evaluation
Bayes Factor
Statistical Models
Unequal
Software Package
Inconsistent
Statistical Model
Range of data

Keywords

  • Bain
  • Bayes factor
  • informative hypotheses
  • multiple populations
  • MODEL SELECTION
  • FORTRAN-90 PROGRAM
  • INEQUALITY

Cite this

@article{93d6db8ca8af46c2b16dfb752df6e2f4,
title = "Bayesian evaluation of informative hypotheses for multiple populations",
abstract = "The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple‐population data can properly be processed. The resulting multiple‐population approximate adjusted fractional Bayes factor is implemented in the R package Bain.",
keywords = "Bain, Bayes factor, informative hypotheses, multiple populations, MODEL SELECTION, FORTRAN-90 PROGRAM, INEQUALITY",
author = "Herbert Hoijtink and Xin Gu and Joris Mulder",
year = "2019",
doi = "10.1111/bmsp.12145",
language = "English",
volume = "72",
pages = "219--243",
journal = "British Journal of Mathematical and Statistical Psychology",
issn = "0007-1102",
publisher = "Wiley",
number = "2",

}

Bayesian evaluation of informative hypotheses for multiple populations. / Hoijtink, Herbert; Gu, Xin; Mulder, Joris.

In: British Journal of Mathematical and Statistical Psychology, Vol. 72, No. 2, 2019, p. 219-243.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Bayesian evaluation of informative hypotheses for multiple populations

AU - Hoijtink, Herbert

AU - Gu, Xin

AU - Mulder, Joris

PY - 2019

Y1 - 2019

N2 - The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple‐population data can properly be processed. The resulting multiple‐population approximate adjusted fractional Bayes factor is implemented in the R package Bain.

AB - The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple‐population data can properly be processed. The resulting multiple‐population approximate adjusted fractional Bayes factor is implemented in the R package Bain.

KW - Bain

KW - Bayes factor

KW - informative hypotheses

KW - multiple populations

KW - MODEL SELECTION

KW - FORTRAN-90 PROGRAM

KW - INEQUALITY

U2 - 10.1111/bmsp.12145

DO - 10.1111/bmsp.12145

M3 - Article

VL - 72

SP - 219

EP - 243

JO - British Journal of Mathematical and Statistical Psychology

JF - British Journal of Mathematical and Statistical Psychology

SN - 0007-1102

IS - 2

ER -