Before revision:  This rule updates an intermediate set of priors that is a linear contraction of the initial set with respect to the set of priors attaining maximum likelihood. The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility  .
After revision:  This rule updates an intermediate set of priors that is a linear contraction of C towards its subset assigning a maximal probability to the observed event. The degree of contraction captures the extent of willingness to discard priors based on likelihood when updating. Two well-known updating rules of multiple priors, Full Bayesian (FB) and Maximum Likelihood (ML), are included as special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility .