4 papers
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…
A Dimension-Independent Bound on the Wasserstein Contraction Rate of a Geodesic Random Walk on the Sphere
Philip Schär, Thilo D. Stier
We theoretically analyze the properties of a geodesic random walk on the Euclidean -sphere. Specifically, we prove that the random walk's transition kernel is Wasserstein contra…
Wasserstein contraction and spectral gap of slice sampling revisited
Philip Schär
We propose a new class of Markov chain Monte Carlo methods, called -polar slice sampling (-PSS), as a technical tool that interpolates between and extrapolates beyond uniform…
Dimension-independent spectral gap of polar slice sampling
Daniel Rudolf, Philip Schär
Polar slice sampling, a Markov chain construction for approximate sampling, performs, under suitable assumptions on the target and initial distribution, provably independent of the…