  An algorithm for sampling from non-log-concave multivariate distributions is
proposed, which improves the adaptive rejection Metropolis sampling (ARMS)
algorithm by incorporating the hit and run sampling. It is not rare that the
ARMS is trapped away from some subspace with significant probability in the
support of the multivariate distribution. While the ARMS updates samples only
in the directions that are parallel to dimensions, our proposed method, the hit
and run ARMS (HARARMS), updates samples in arbitrary directions determined by
the hit and run algorithm, which makes it almost not possible to be trapped in
any isolated subspaces. The HARARMS performs the same as ARMS in a single
dimension while more reliable in multidimensional spaces. Its performance is
illustrated by a Bayesian free-knot spline regression example. We showed that
it overcomes the well-known `lethargy' property and decisively find the global
optimal number and locations of the knots of the spline function.
