Mutation testing is a fault-based testing technique for measuring the adequacy of a test suite. Test suites are assigned scores based on their ability to expose synthetic faults (i.e., mutants) generated by a range of well-defined mathematical operators. The test suites can then be augmented to expose the mutants that remain undetected and are not semantically equivalent to the original code. However, the mutation score can be increased superfluously by mutants that are easy to expose. In addition, it is infeasible to examine all the mutants generated by a large set of mutation operators. Existing approaches have therefore focused on determining the sufficient set of mutation operators and the set of equivalent mutants. Instead, this paper proposes a novel Bayesian approach that prioritizes operators whose mutants are likely to remain unexposed by the existing test suites. Probabilistic sampling methods are adapted to iteratively examine a subset of the available mutants and direct focus towards the more informative operators. Experimental results show that the proposed approach identifies more than 90% of the important operators by examining ≤ 20% of the available mutants, and causes a 6% increase in the importance measure of the selected mutants.