Statistical inference as Green's functions

TL;DR

This paper demonstrates that statistical inference for exchangeable binary data can be objectively described using Green's functions derived from de Finetti's theorem, offering a new theoretical foundation for objectivity in data analysis.

Contribution

It introduces a novel approach linking statistical inference to Green's functions via differential equations, providing an objective framework based on data.

Findings

Derivation of a differential equation from de Finetti's theorem

Statistical inference expressed through Green's functions

Establishes an objective basis for inference in exchangeable data

Abstract

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.