A practical and efficient approach for Bayesian quantum state estimation
Bayesian inference is a powerful paradigm for quantum state tomography, treating
uncertainty in meaningful and informative ways. Yet the numerical challenges associated …
uncertainty in meaningful and informative ways. Yet the numerical challenges associated …
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
Certified dimension reduction in nonlinear Bayesian inverse problems
We propose a dimension reduction technique for Bayesian inverse problems with nonlinear
forward operators, non-Gaussian priors, and non-Gaussian observation noise. The …
forward operators, non-Gaussian priors, and non-Gaussian observation noise. The …
An optimization of artificial neural network modeling methodology for the reliability assessment of corroding natural gas pipelines
K Wen, L He, J Liu, J Gong - Journal of Loss Prevention in the Process …, 2019 - Elsevier
A fast calculation of the reliability is meaningful to the in-line inspection of corroding natural
gas pipelines. However, the traditional Monte Carlo simulation (MCS) method is time …
gas pipelines. However, the traditional Monte Carlo simulation (MCS) method is time …
Accounting for model error in Bayesian solutions to hydrogeophysical inverse problems using a local basis approach
C Köpke, J Irving, AH Elsheikh - Advances in water resources, 2018 - Elsevier
Bayesian solutions to geophysical and hydrological inverse problems are dependent upon a
forward model linking subsurface physical properties to measured data, which is typically …
forward model linking subsurface physical properties to measured data, which is typically …
MALA-within-Gibbs samplers for high-dimensional distributions with sparse conditional structure
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 …
from a given target probability distribution. We discuss one particular MCMC sampler, the …
Multilevel sequential Monte Carlo with dimension-independent likelihood-informed proposals
In this article we develop a new sequential Monte Carlo method for multilevel Monte Carlo
estimation. In particular, the method can be used to estimate expectations with respect to a …
estimation. In particular, the method can be used to estimate expectations with respect to a …
[HTML][HTML] Covariance-based MCMC for high-dimensional Bayesian updating with Sequential Monte Carlo
B Carrera, I Papaioannou - Probabilistic Engineering Mechanics, 2024 - Elsevier
Abstract Sequential Monte Carlo (SMC) is a reliable method to generate samples from the
posterior parameter distribution in a Bayesian updating context. The method samples a …
posterior parameter distribution in a Bayesian updating context. The method samples a …
Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors
Motivated by Bayesian inference with highly informative data we analyze the performance of
random walk-like Metropolis-Hastings algorithms for approximate sampling of increasingly …
random walk-like Metropolis-Hastings algorithms for approximate sampling of increasingly …
Lithological tomography with the correlated pseudo-marginal method
We consider lithological tomography in which the posterior distribution of (hydro) geological
parameters of interest is inferred from geophysical data by treating the intermediate …
parameters of interest is inferred from geophysical data by treating the intermediate …