74 citations · 142 across the 17 of their papers we have counts for
6 papers · 1 filter
Delayed Acceptance Slice Sampling
Kevin Bitterlich, Daniel Rudolf, Björn Sprungk
Slice sampling is a well-established Markov chain Monte Carlo method for approximate sampling of target distributions which are only known up to a normalizing constant. The method…
Weak Poincaré inequality comparisons for ideal and hybrid slice sampling
Sam Power, Daniel Rudolf, Björn Sprungk +1
Using the framework of weak Poincaré inequalities, we provide a general comparison between Hybrid and Ideal Slice Sampling in terms of their corresponding Dirichlet forms. In parti…
Metropolis-adjusted interacting particle sampling
Björn Sprungk, Simon Weissmann, Jakob Zech
In recent years, various interacting particle samplers have been developed to sample from complex target distributions, such as those found in Bayesian inverse problems. These samp…
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…
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…
On a generalization of the preconditioned Crank-Nicolson Metropolis algorithm
Daniel Rudolf, Björn Sprungk
Metropolis algorithms for approximate sampling of probability measures on infinite dimensional Hilbert spaces are considered and a generalization of the preconditioned Crank-Nicols…