Predictive Inference

Predictive Inference is a framework in statistics and machine learning that treats the prediction of future observations as the primary goal of inference, rather than the estimation of hidden parameters or the testing of hypotheses. In contrast to the parameter-centric tradition of classical statistics, predictive inference asks: given what I have seen, what should I expect to see next?

This orientation shifts the focus from truth to action. A parameter is an abstraction; a prediction is a commitment. The Bayesian tradition — particularly the work of Bruno de Finetti on exchangeability — provides the most natural formalism for predictive inference, since Bayesian updating is inherently a procedure for revising predictive distributions in light of new evidence. Predictive inference is also central to the modern theory of Conformal Prediction, which provides distribution-free guarantees on prediction coverage.

Predictive inference is the future of epistemology. A theory that cannot tell you what to expect tomorrow is not a theory of learning — it is a theory of storytelling.