A Bayesian approach to solving inverse problems provides insight regarding model limitations as well as the underlying model and observation uncertainty. In this paper we introduce pymcmcstat, which provides a wide variety of tools for estimating unknown parameter distributions. For scientists and engineers familiar with least-squares optimization, this package provides a similar interface from which to expand their analysis to a Bayesian framework. This package has been utilized in a wide array of scientific and engineering problems, including radiation source localization and constitutive model development of smart material systems.

Keywords:Markov Chain Monte Carlo (MCMC)Delayed Rejection Adaptive Metropolis (DRAM)Parameter EstimationBayesian Inference