118 citations · 323 across the 18 of their papers we have counts for
20 papers
Numerical study of high-dimensional covariance estimation and localization for data assimilation
Shay Gilpin, Matthias Morzfeld, Kevin K. Lin
Covariance localization is a critical component of ensemble-based data assimilation (DA) and many current localization schemes simply dampen correlations as a function of distance.…
Localized covariance estimation: A Bayesian perspective
Robert J. Webber, Matthias Morzfeld
A major problem in numerical weather prediction (NWP) is the estimation of high-dimensional covariance matrices from a small number of samples. Maximum likelihood estimators cannot…
Can one use Earth's magnetic axial dipole field intensity to predict reversals?
Kyle Gwirtz, Matthias Morzfeld, Alexandre Fournier +1
We study predictions of reversals of Earth's axial magnetic dipole field that are based solely on the dipole's intensity. The prediction strategy is, roughly, that once the dipole…
MALA-within-Gibbs samplers for high-dimensional distributions with sparse conditional structure
X. T. Tong, M. Morzfeld, Y. M. Marzouk
Markov chain Monte Carlo (MCMC) samplers are numerical methods for drawing samples from a given target probability distribution. We discuss one particular MCMC sampler, the MALA-wi…
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…
Localization for MCMC: sampling high-dimensional posterior distributions with local structure
Matthias Morzfeld, Xin T. Tong, Youssef M. Marzouk
We investigate how ideas from covariance localization in numerical weather prediction can be used in Markov chain Monte Carlo (MCMC) sampling of high-dimensional posterior distribu…
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…
Sampling, feasibility, and priors in Bayesian estimation
Alexandre J. Chorin, Fei Lu, Robert N. Miller +2
Importance sampling algorithms are discussed in detail, with an emphasis on implicit sampling, and applied to data assimilation via particle filters. Implicit sampling makes it pos…
Analysis of the ensemble Kalman filter for marginal and joint posteriors
Matthias Morzfeld, Daniel Hodyss
The ensemble Kalman filter (EnKF) is widely used to sample a probability density function (pdf) generated by a stochastic model conditioned by noisy data. This pdf can be either a…
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…
Path integral formulation of stochastic optimal control with generalized costs
Insoon Yang, Matthias Morzfeld, Claire J. Tomlin +1
Path integral control solves a class of stochastic optimal control problems with a Monte Carlo (MC) method for an associated Hamilton-Jacobi-Bellman (HJB) equation. The MC approach…
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 sampling for path integral control, Monte Carlo localization, and SLAM
Matthias Morzfeld
The applicability and usefulness of implicit sampling in stochastic optimal control, stochastic localization, and simultaneous localization and mapping (SLAM), is explored; implici…
Conditions for successful data assimilation
Alexandre J. Chorin, Matthias Morzfeld
We show, using idealized models, that numerical data assimilation can be successful only if an effective dimension of the problem is not excessive. This effective dimension depends…
Implicit particle methods and their connection with variational data assimilation
Ethan Atkins, Matthias Morzfeld, Alexandre J. Chorin
The implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that include…
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…