  Statistical inference from data is a foundational task in science. Recently,
it has received growing attention for its central role in inference systems of
primary interest in data sciences and machine learning. However, the
understanding of statistical inference is not that solid while remains as a
matter of subjective belief or as the routine procedures once claimed
objective. We here show that there is an objective description of statistical
inference for long sequence of exchangeable binary random variables, the
prototypal stochasticity in theories and applications. A linear differential
equation is derived from the identity known as de Finetti's representation
theorem, and it turns out that statistical inference is given by the Green's
functions. Our finding is an answer to the normative issue of science that
pursues the objectivity based on data, and its significance will be
far-reaching in most pure and applied fields.
