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Found 2 papers, 1 papers with code
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference
The algorithm aims to mitigate some of the hurdles faced by traditional Markov Chain Monte Carlo (MCMC) samplers, through constructing proposal probability densities that are both, easy to sample and that provide a better approximation to the target density than a simple Gaussian proposal distribution would.
emcee: The MCMC Hammer
The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample).
Instrumentation and Methods for Astrophysics Computational Physics Computation
