Extended Rosenbrock-Type Densities for Markov Chain Monte Carlo
(MCMC) Sampler Benchmarking
Description
New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested
and their performance accurately assessed. This requires densities
that offer challenging properties to the novel sampling algorithms.
One such popular problem is the Rosenbrock function. However, while its
shape lends itself well to a benchmark problem, no codified multivariate expansion
of the density exists. We have developed an extension to this class of distributions
and supplied densities and direct sampler functions to assess the performance
of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock
Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) .