2 citations · 4 across the 5 of their papers we have counts for
6 papers
Wasserstein de-initialization for Markov chains
Mareike Hasenpflug
This article generalizes the de-initialization framework, proposed by (Roberts and Rosenthal, 2001) for total variation distance, to Wasserstein distances. In essence, de-initializ…
Uniform ergodicity of geodesic slice sampling
Mareike Hasenpflug
Geodesic slice sampling, introduced in Durmus et al., 2024, is a slice sampling based Markov chain Monte Carlo method for approximate sampling from distributions on Riemannian mani…
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…
Geodesic slice sampling on the sphere
Michael Habeck, Mareike Hasenpflug, Shantanu Kodgirwar +1
Probability measures on the sphere form an important class of statistical models and are used, for example, in modeling directional data or shapes. Due to their widespread use, but…
Reversibility of elliptical slice sampling revisited
Mareike Hasenpflug, Viacheslav Telezhnikov, Daniel Rudolf
We extend elliptical slice sampling, a Markov chain transition kernel suggested in Murray, Adams and MacKay 2010, to infinite-dimensional separable Hilbert spaces and discuss its w…
Wasserstein convergence rates of increasingly concentrating probability measures
Mareike Hasenpflug, Daniel Rudolf, Björn Sprungk
For we consider the sequence of probability measures , where is determined by a density that is…