DISCUSSION
A load of literature published on Bayesian inference has proved its popularity among researches while its concept is not straightforward for amateur learners (14). The purpose of our paper was to provide a comprehensive framework with illuminating examples to shed light upon the concept of some of Bayesian mechanisms of sampling. We showed that alternative approaches which are intuitively appealing and easy-to-understand work well in case of low-dimensional problems and appropriate Prior information such as weighted prior, otherwise MCMC is a Trouble-free tool. Although its concept is not an intuitively realizable advanced method of MCMC tackles most complex issues. Different studies tried to cover Bayesian statistical approach as a need specifically for many sciences (15-26). Also, DA method as an alternative approach gives researchers more tangible sense in the role of prior and data for inference making, the posterior calculation is simple using this method. We tried to cover the sufficient methods of Bayesian simulation approaches with some clear examples and provide an introductory work of Bayesian foundation; R software codes are available in the Supplementary as well.