3 papers
stat.CO2020
Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems
Johnathan Bardsley, Tiangang Cui
In many hierarchical inverse problems, not only do we want to estimate high- or infinite-dimensional model parameters in the parameter-to-observable maps, but we also have to estim…
stat.CO2019
Scalable optimization-based sampling on function space
Johnathan Bardsley, Tiangang Cui, Youssef Marzouk +1
Optimization-based samplers such as randomize-then-optimize (RTO) [2] provide an efficient and parallellizable approach to solving large-scale Bayesian inverse problems. These meth…
astro-ph2004
Least-squares methods with Poissonian noise: an analysis and a comparison with the Richardson-Lucy algorithm
R. Vio, J. Bardsley, W. Wamsteker
It is well-known that the noise associated with the collection of an astronomical image by a CCD camera is, in large part, Poissonian. One would expect, therefore, that computation…