  We demonstrate that the functional form of the likelihood contains a
sufficient amount of information for constructing a prior for the unknown
parameters. We develop a four-step algorithm by invoking the information
entropy as the measure of uncertainty and show how the information gained from
coarse-graining and resolving power of the likelihood can be used to construct
the likelihood-induced priors. As a consequence, we show that if the data model
density belongs to the exponential family, the likelihood-induced prior is the
conjugate prior to the corresponding likelihood.
