3 papers
math.ST2026
On the mixing properties of some preconditioned multiproposal Markov Chain Monte Carlo algorithms
Giulia Carigi, Nathan E. Glatt-Holtz, Cecilia F. Mondaini +1
We study two recently discovered "dimension-free" Monte Carlo sampling algorithms, the multiproposal and multiple-try preconditioned Crank-Nicolson methods (mpCN and MTpCN). These…
stat.CO2026
Multiproposal Elliptical Slice Sampling
Guillermina Senn, Nathan Glatt-Holtz, Giulia Carigi +2
We introduce Multiproposal Elliptical Slice Sampling, a self-tuning multiproposal Markov chain Monte Carlo method for Bayesian inference with Gaussian priors. Our method generalize…
stat.CO2026
Bayesian Semi-Blind Deconvolution at Scale
Guillermina Senn, HÃ¥kon Tjelmeland, Nathan Glatt-Holtz +2
Blind image deconvolution refers to the problem of simultaneously estimating the blur kernel and the true image from a set of observations when both the blur kernel and the true im…