Polar slice sampling, a Markov chain construction for approximate sampli...
Polar slice sampling (Roberts Rosenthal, 2002) is a Markov chain app...
Probability measures that are constrained to the sphere form an importan...
We discuss the well-definedness of elliptical slice sampling, a Markov c...
For integrable functions we provide a weak law of large numbers for
stru...
Motivated by Bayesian inference with highly informative data we analyze ...
We investigate the stability of quasi-stationary distributions of killed...
For Bayesian learning, given likelihood function and Gaussian prior, the...
In this paper we prove upper and lower bounds on the minimal spherical
d...
We propose and investigate a hidden Markov model (HMM) for the analysis ...
Doubly-intractable distributions appear naturally as posterior distribut...
The dispersion of a point set in [0,1]^d is the volume of the largest ax...
We prove Wasserstein contraction of simple slice sampling for approximat...
Importance sampling Monte-Carlo methods are widely used for the approxim...
The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a
per...
We want to compute the integral of a function or the expectation of a ra...
A classical approach for approximating expectations of functions w.r.t.
...
We consider parameter estimation in hidden finite state space Markov mod...