118 citations · 323 across the 18 of their papers we have counts for
9 papers · 1 filter
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…
Symmetrized importance samplers for stochastic differential equations
Andrew Leach, Kevin K. Lin, Matthias Morzfeld
We study a class of importance sampling methods for stochastic differential equations (SDEs). A small-noise analysis is performed, and the results suggest that a simple symmetrizat…
Iterative importance sampling algorithms for parameter estimation
Matthias Morzfeld, Marcus S. Day, Ray W. Grout +3
In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov Chain Mont…
What the collapse of the ensemble Kalman filter tells us about particle filters
Matthias Morzfeld, Daniel Hodyss, Chris Snyder
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle fi…
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…
Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems
Fei Lu, Matthias Morzfeld, Xuemin Tu +1
Polynomial chaos expansions are used to reduce the computational cost in the Bayesian solutions of inverse problems by creating a surrogate posterior that can be evaluated inexpens…
Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation
Matthias Morzfeld, Alexandre J. Chorin
Implicit particle filtering is a sequential Monte Carlo method for data assim- ilation, designed to keep the number of particles manageable by focussing attention on regions of lar…
A random map implementation of implicit filters
Matthias Morzfeld, Xuemin Tu, Ethan Atkins +1
Implicit particle filters for data assimilation generate high-probability samples by representing each particle location as a separate function of a common reference variable. This…
Implicit particle filters for data assimilation
Alexandre J. Chorin, Matthias Morzfeld, Xuemin Tu
Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles…