2 citations · 2 across the 3 of their papers we have counts for
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stat.CO2022★ 2 cited
Robust random walk-like Metropolis-Hastings algorithms for concentrating posteriors
Daniel Rudolf, Björn Sprungk
Motivated by Bayesian inference with highly informative data we analyze the performance of random walk-like Metropolis-Hastings algorithms for approximate sampling of increasingly…
stat.CO2018
Perturbation Bounds for Monte Carlo within Metropolis via Restricted Approximations
Felipe Medina-Aguayo, Daniel Rudolf, Nikolaus Schweizer
The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target dis…
stat.CO2018
On a Metropolis-Hastings importance sampling estimator
Daniel Rudolf, Björn Sprungk
A classical approach for approximating expectations of functions w.r.t. partially known distributions is to compute the average of function values along a trajectory of a Metropoli…