12.4k citations · 12.6k across the 9 of their papers we have counts for
11 papers
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference
Leen Alawieh, Jonathan Goodman, John B. Bell
A new algorithm is developed to tackle the issue of sampling non-Gaussian model parameter posterior probability distributions that arise from solutions to Bayesian inverse problems…
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…
The Effect of Noise on the Propagating Speed of Pre-mixed Laminar Flame Fronts
Hongliang Liu, Jonathan Goodman
We study the effect of thermal noise on the propagation speed of a planar flame. We show that this out of equilibrium greatly amplifies the effect of thermal noise to yield macrosc…
A Bifurcation Monte Carlo Scheme for Rare Event Simulation
Hongliang Liu, Jonathan Goodman
The bifurcation method is a way to do rare event sampling -- to estimate the probability of events that are too rare to be found by direct simulation. We describe the bifurcation m…
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…