The Markov Chain Monte Carlo (MCMC) is a computer-driven sampling method. It allows one to characterize a distribution without knowing all of the distribution’s mathematical properties by randomly sampling values out of the distribution. A particular strength of MCMC is that it can be used to draw samples from distributions even when all that is known about the distribution is how to calculate the density for different samples. This implementation is Population MCMC using Parallel Tempering. Multi-chain implementation allows to generate samples from multi-mode distribution