3 citations · 3 across the 5 of their papers we have counts for
5 papers
Error bounds for simultaneous Wasserstein contractive adaptive increasingly rare MCMC
Julian Hofstadler, Daniel Rudolf
We investigate adaptive increasingly rare Markov chain Monte Carlo algorithms and the associated time-average estimator for approximating expectations. Under a simultaneous Wassers…
Perturbations of Markov Chains
Daniel Rudolf, Aaron Smith, Matias Quiroz
This chapter surveys progress on three related topics in perturbations of Markov chains: the motivating question of when and how "perturbed" MCMC chains are developed, the theoreti…
Almost sure convergence rates of adaptive increasingly rare Markov chain Monte Carlo
Julian Hofstadler, Krzysztof Latuszynski, Gareth O. Roberts +1
We consider adaptive increasingly rare Markov chain Monte Carlo (MCMC) algorithms, which are adaptive MCMC methods, where the adaptation concerning the "past'' happens less and les…
Parallel Affine Transformation Tuning of Markov Chain Monte Carlo
Philip Schär, Michael Habeck, Daniel Rudolf
The performance of Markov chain Monte Carlo samplers strongly depends on the properties of the target distribution such as its covariance structure, the location of its probability…
Geodesic slice sampling on Riemannian manifolds
Alain Durmus, Samuel Gruffaz, Mareike Hasenpflug +1
We propose a theoretically justified and practically applicable slice sampling based Markov chain Monte Carlo (MCMC) method for approximate sampling from probability measures on Ri…