Nowadays, the volume of science or engineering data has increased substantially, and a variety of models have been developed to help to understand the observations. Markov chain Monte Carlo (MCMC) has been established as the standard procedure of inferring these model parameters subject to the available data in a Bayesian framework. Real systems such as interacting galaxies require complex models and these models are computationally prohibitive. The goal of this project is to provide a flexible platform for connecting a range of efficient algorithms for any user-defined circumstances. It will also serve as a testbed for assessing new state-of-the-art model-fitting algorithms.

The most commonly used MCMC methods are variants of the Metropolis-Hastings (MH) algorithm. At the beginning of this project and in this article, the standard MH-MCMC algorithm together with affine-invariant ensemble MCMC, which has dominated astronomical analysis over the past few decades, has been tested to reveal the performance of each sampler for the problems with known solutions. The Hamiltonian Monte Carlo algorithm was also tested and it shows in which circumstance that it outperforms the other two.