  In machine learning and statistics, probabilistic inference involving
multimodal distributions is quite difficult. This is especially true in high
dimensional problems, where most existing algorithms cannot easily move from
one mode to another. To address this issue, we propose a novel Bayesian
inference approach based on Markov Chain Monte Carlo. Our method can
effectively sample from multimodal distributions, especially when the dimension
is high and the modes are isolated. To this end, it exploits and modifies the
Riemannian geometric properties of the target distribution to create
\emph{wormholes} connecting modes in order to facilitate moving between them.
Further, our proposed method uses the regeneration technique in order to adapt
the algorithm by identifying new modes and updating the network of wormholes
without affecting the stationary distribution. To find new modes, as opposed to
rediscovering those previously identified, we employ a novel mode searching
algorithm that explores a \emph{residual energy} function obtained by
subtracting an approximate Gaussian mixture density (based on previously
discovered modes) from the target density function.
