An intuitive framework for Bayesian posterior simulation methods

Global Epidemiology

Razieh Bidhendi Yarandi¹  Kazem Mohammad²  Mohammad Ali Mansournia²  Hojjat Zeraati² 

[1] Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran;Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran;

关键词: Bayesian methods;    Data augmentation;    Importance sampling;    MCMC;    Rejection sampling;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown