  An agent often has a number of hypotheses, and must choose among them based
on observations, or outcomes of experiments. Each of these observations can be
viewed as providing evidence for or against various hypotheses. All the
attempts to formalize this intuition up to now have assumed that associated
with each hypothesis h there is a likelihood function \mu_h, which is a
probability measure that intuitively describes how likely each observation is,
conditional on h being the correct hypothesis. We consider an extension of this
framework where there is uncertainty as to which of a number of likelihood
functions is appropriate, and discuss how one formal approach to defining
evidence, which views evidence as a function from priors to posteriors, can be
generalized to accommodate this uncertainty.
