Aims: Caustic-crossing binary-lens microlensing events are important
anomalous events, because they may reveal an extrasolar planet companion
orbiting the lens star. Fast and robust modelling methods are thus of prime
interest to quickly conclude on the possible planetary nature of the event.
Cassan (2008) introduced a new set of parameters to model binary-lens events,
which are closely related to the features observed in the light curve. In this
work, we explain how Bayesian priors can be added in this framework, and
investigate on possible interesting choices. Methods: We develop a mathematical
formulation that allows to compute analytically priors on the new parameters,
given some prior knowledge on other physical quantities. We explicitely compute
the priors for a number of interesting cases, and show how this can be
implemented in a fully Bayesian, Markov-Chain Monte-Carlo algorithm. Results:
Using Bayesian priors can speed up microlens fitting codes by reducing time
spent on physically implausible models, and helps to discriminate among
alternative models based on the physical plausibility of their parameters.
