Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiency from such a distribution, location-scale transformation and a transformation to polar coordinates are used.

This paper develops an extension of the Riemann sum techniques of Philippe (1997b) in the setup of MCMCC algorithms. It shows that the technique applies equally well to the output of these algorithms, with similar speeds of convergence which improve upon the regular estimator. The restriction on...