  We introduce a new Markov-Chain Monte Carlo (MCMC) approach designed for
efficient sampling of highly correlated and multimodal posteriors. Parallel
tempering, though effective, is a costly technique for sampling such
posteriors. Our approach minimizes the use of parallel tempering, only using it
for a short time to tune a new jump proposal. For complex posteriors we find
efficiency improvements up to a factor of ~13. The estimation of parameters of
gravitational-wave signals measured by ground-based detectors is currently done
through Bayesian inference with MCMC one of the leading sampling methods.
Posteriors for these signals are typically multimodal with strong non-linear
correlations, making sampling difficult. As we enter the advanced-detector era,
improved sensitivities and wider bandwidths will drastically increase the
computational cost of analyses, demanding more efficient search algorithms to
meet these challenges.
