We provide a characterization
of virtual Bayesian implementation in environments satisfying no-total-indifference.
A social choice function in such environments is virtually Bayesian implementable
if and only if it satisfies incentive compatibility and a condition we term
virtual monotonicity. The latter is weaker than Bayesian monotonicity - a condition
which is known to be necessary for Bayesian implementation. We argue that virtual
monotonicity is weak in the sense that it is generically satisfied in environments
with at least three alternatives. This implies that in most environments virtual
Bayesian implementation is as successful as it can be (incentive compatibility
is the only condition needed).