The Probabilities of Orbital-Companion Models for Stellar Radial Velocity Data

Understand

The fully marginalized likelihood, or Bayesian evidence, is of great importance in probabilistic data analysis, because it is involved in calculating the posterior probability of a model or re-weighting a mixture of models conditioned on data.

• It is, however, extremely challenging to compute.
• This paper presents a geometric-path Monte Carlo method, inspired by multi-canonical Monte Carlo to evaluate the fully marginalized likelihood.
• We show that the algorithm is very fast and easy to implement and produces a justified uncertainty estimate on the fully marginalized likelihood.