An intuitive framework for Bayesian posterior simulation methods
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for...
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doaj-b132b32508574e468161b43e51d14d772021-08-22T04:31:08ZengElsevierGlobal Epidemiology2590-11332021-11-013100060An intuitive framework for Bayesian posterior simulation methodsRazieh Bidhendi Yarandi0Mohammad Ali Mansournia1Hojjat Zeraati2Kazem Mohammad3Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; Corresponding author.Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for non-statisticians. This paper aims to provide an intuitive framework of four essential Bayesian computational methods for epidemiologists and other health researchers. We do not cover an extensive mathematical discussion of these approaches, but instead offer a non-quantitative description of these algorithms and provide some illuminating examples. Materials and methods: Bayesian computational methods, namely importance sampling, rejection sampling, Markov chain Monte Carlo, and data augmentation are presented. Results and conclusions: The substantial amount of research published on Bayesian inference has highlighted its popularity among researchers, while the basic concepts are not always straightforward for interested learners. We show that alternative approaches such as a weighted prior approach, which are intuitively appealing and easy-to-understand, work well in the case of low-dimensional problems and appropriate prior information. Otherwise, MCMC is a trouble-free tool in those cases.http://www.sciencedirect.com/science/article/pii/S2590113321000146Bayesian methodsData augmentationImportance samplingMCMCRejection sampling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Razieh Bidhendi Yarandi Mohammad Ali Mansournia Hojjat Zeraati Kazem Mohammad |
spellingShingle |
Razieh Bidhendi Yarandi Mohammad Ali Mansournia Hojjat Zeraati Kazem Mohammad An intuitive framework for Bayesian posterior simulation methods Global Epidemiology Bayesian methods Data augmentation Importance sampling MCMC Rejection sampling |
author_facet |
Razieh Bidhendi Yarandi Mohammad Ali Mansournia Hojjat Zeraati Kazem Mohammad |
author_sort |
Razieh Bidhendi Yarandi |
title |
An intuitive framework for Bayesian posterior simulation methods |
title_short |
An intuitive framework for Bayesian posterior simulation methods |
title_full |
An intuitive framework for Bayesian posterior simulation methods |
title_fullStr |
An intuitive framework for Bayesian posterior simulation methods |
title_full_unstemmed |
An intuitive framework for Bayesian posterior simulation methods |
title_sort |
intuitive framework for bayesian posterior simulation methods |
publisher |
Elsevier |
series |
Global Epidemiology |
issn |
2590-1133 |
publishDate |
2021-11-01 |
description |
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account for uncertainty in inference decision-making. Various estimation methods have been introduced to implement Bayesian methods. Although these algorithms are powerful, they are not always easy to grasp for non-statisticians. This paper aims to provide an intuitive framework of four essential Bayesian computational methods for epidemiologists and other health researchers. We do not cover an extensive mathematical discussion of these approaches, but instead offer a non-quantitative description of these algorithms and provide some illuminating examples. Materials and methods: Bayesian computational methods, namely importance sampling, rejection sampling, Markov chain Monte Carlo, and data augmentation are presented. Results and conclusions: The substantial amount of research published on Bayesian inference has highlighted its popularity among researchers, while the basic concepts are not always straightforward for interested learners. We show that alternative approaches such as a weighted prior approach, which are intuitively appealing and easy-to-understand, work well in the case of low-dimensional problems and appropriate prior information. Otherwise, MCMC is a trouble-free tool in those cases. |
topic |
Bayesian methods Data augmentation Importance sampling MCMC Rejection sampling |
url |
http://www.sciencedirect.com/science/article/pii/S2590113321000146 |
work_keys_str_mv |
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