Purpose: Bayesian inference has become very popular science. It offers some pragmatic approaches to account for uncertainty in inference-decision making. Various estimation methods have been introduced to implement Bayesian methods but although these algorithms are powerful they are not always easy to grasp. This paper aims to provide an intuitive framework of four key Bayesian computational methods for researchers in clinical studies. We will not cover daunting mathematical discussion of these approaches, but rather offer a non-quantitative description of these algorithms and provide some illuminating examples. Materials and methods: Bayesian computational methods namely, i) Importance sampling, ii) Rejection sampling, iii) Markov-chain Monte Carlo, iv) Data augmentation were introduced. Results and conclusions: A load of literature published on Bayesian inference has proved its popularity among researches while its concept is not straightforward for amateur learners. 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.