  The Rosenbrock function is an ubiquitous benchmark problem for numerical
optimisation, and variants have been proposed to test the performance of Markov
Chain Monte Carlo algorithms. In this work we discuss the two-dimensional
Rosenbrock density, its current $n$-dimensional extensions, and their
advantages and limitations. We then propose a new extension to arbitrary
dimensions called the Hybrid Rosenbrock distribution, which is composed of
conditional normal kernels arranged in such a way that preserves the key
features of the original kernel. Moreover, due to its structure, the Hybrid
Rosenbrock distribution is analytically tractable and possesses several
desirable properties, which make it an excellent test model for computational
algorithms.
