  Prediction of future observations is a fundamental problem in statistics.
Here we present a general approach based on the recently developed inferential
model (IM) framework. We employ an IM-based technique to marginalize out the
unknown parameters, yielding prior-free probabilistic prediction of future
observables. Verifiable sufficient conditions are given for validity of our IM
for prediction, and a variety of examples demonstrate the proposed method's
performance. Thanks to its generality and ease of implementation, we expect
that our IM-based method for prediction will be a useful tool for
practitioners.
