1 citations · 1 across the 3 of their papers we have counts for
9 papers
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…
Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +1
Multiproposal MCMC (MP-MCMC) algorithms use clouds of proposals to efficiently traverse state spaces and overcome complex target geometries. While MCMC methods are embarrassingly p…
The short memory limit for long time statistics in a stochastic Coleman-Gurtin model of heat conduction
Nathan E. Glatt-Holtz, Vincent R. Martinez, Hung D. Nguyen
We consider a class of semi-linear differential Volterra equations with polynomial-type potentials that incorporates the effects of memory while being subjected to random perturbat…
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…
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…
Existence and higher regularity of statistically steady states for the stochastic Coleman-Gurtin equation
Nathan E. Glatt-Holtz, Vincent R. Martinez, Hung D. Nguyen
We study a class of semi-linear differential Volterra equations with polynomial-type potentials that incorporates the effects of memory while being subjected to random perturbation…