  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 and its tail behavior. We explore the use of
bijective affine transformations of the sample space to improve the properties
of the target distribution and thereby the performance of samplers running in
the transformed space. In particular, we propose a flexible and user-friendly
scheme for adaptively learning the affine transformation during sampling.
Moreover, the combination of our scheme with Gibbsian polar slice sampling is
shown to produce samples of high quality at comparatively low computational
cost in several settings based on real-world data.
