  A Bayesian re-analysis of published radial velocity data sets is providing
evidence for additional planetary candidates. The nonlinear model fitting is
accomplished with a new hybrid Markov chain Monte Carlo (HMCMC) algorithm which
incorporates parallel tempering, simulated annealing and genetic crossover
operations. Each of these features facilitate the detection of a global minimum
in chi^2. By combining all three, the HMCMC greatly increases the probability
of realizing this goal. When applied to the Kepler problem it acts as a
powerful multi-planet Kepler periodogram for both parameter estimation and
model selection. The HMCMC algorithm is embedded in a unique two stage adaptive
control system that automates the tuning of the MCMC proposal distributions
through an annealing operation.
