INTRODUCTION
The growth in the application of Bayesian analysis in the sciences has
created a need to present its complex concepts in a more understandable
language for non-statisticians.
To many amateur users, their
computationally-intensive simulation approaches has the appearance of a
“black box”. This article aims to describe essential approaches used
in Bayesian methods for posterior simulation in an intuitive manner.
Four Bayesian computational methods are presented: Importance
Sampling (IS) , Rejection sampling (RS), Markov Chain Monte
Carlo (MCMC) and Data Augmentation (DA) . In this study, we aim
to provide a comprehensive, yet easy to follow explanation of these
techniques to make them practical for researchers. Some illuminating
examples are presented to illustrate the algorithms and the concepts
they embody. R software code and some other information are available in
the Supplementary file as well.