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.