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A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters
John Bell, Marcus Day, Jonathan Goodman +2
First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifi…
Monte Carlo on manifolds: sampling densities and integrating functions
Emilio Zappa, Miranda Holmes-Cerfon, Jonathan Goodman
We describe and analyze some Monte Carlo methods for manifolds in Euclidean space defined by equality and inequality constraints. First, we give an MCMC sampler for probability dis…
Small-noise analysis and symmetrization of implicit Monte Carlo samplers
Jonathan Goodman, Kevin K. Lin, Matthias Morzfeld
Implicit samplers are algorithms for producing independent, weighted samples from multi-variate probability distributions. These are often applied in Bayesian data assimilation alg…
Coupling Control Variates for Markov Chain Monte Carlo
Jonathan B. Goodman, Kevin K. Lin
We show that Markov couplings can be used to improve the accuracy of Markov chain Monte Carlo calculations in some situations where the steady-state probability distribution is not…