Published in ‘Bayesian Inference and Maximum Entropy Methods in

Abstract. Astronomers searching for the small signals induced by planets inevitably face signifi-cant statistical challenges. Bayesian inference has the potential of improving the interpretation of existing observations, the planning of future observations and ultimately inferences concerning the overall population of planets. This paper illustrates how a re-analysis of published radial velocity data sets with a Bayesian multi-planet Kepler periodogram is providing strong evidence for addi-tional planetary candidates. The periodogram is implemented with a Markov chain Monte Carlo (MCMC) algorithm that employs an automated adaptive control system. For HD 11964, the data has been re-analyzed using 1, 2, 3 and 4 planet models. The most probable model exhibits three pe-riods of 38.02+0.06−0.05, 360 +