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 …

We extend elliptical slice sampling, a Markov chain transition kernel suggested in Murray,
Adams and MacKay (In The Proceedings of the 13th International Conference on Artificial …

We consider Bayesian analysis on high‐dimensional spheres with angular central Gaussian
priors. These priors model antipodally symmetric directional data, are easily defined in …

Using the framework of weak Poincar {\'e} inequalities, we provide a general comparison
between the Hybrid and Ideal Slice Sampling Markov chains in terms of their Dirichlet forms …

For ℓ: R d→[0,∞) we consider the sequence of probability measures (μ n) n∈ N, where μ n
is determined by a density that is proportional to exp (− n ℓ). We allow for infinitely many …

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 mass …

We propose a theoretically justified and practically applicable slice sampling based Markov
chain Monte Carlo (MCMC) method for approximate sampling from probability measures on …