Approximate equivalence relations
Ehud Hrushovski

TL;DR
This paper develops a comprehensive framework for approximate equivalence relations in probability logic, classifies correlations, and proves model-theoretic theorems with applications to geometry and automorphism groups.
Contribution
It introduces a new classification of correlations, extends categoricity results, and generalizes the Lie model theorem for approximate subgroups within a unified measure-theoretic logic framework.
Findings
Classification of binary correlations via Kim-Pillay space
Model-theoretic proof of a categoricity theorem for measure structures
Structure theorem for sequences of approximate equivalence relations
Abstract
We study approximate equivalence relations up to commensurability, in the presence of a definable measure. As a basic framework, we give a presentation of probability logic based on continuous logic. Hoover's normal form is valid here; if one begins with a discrete logic structure, it reduces arbitrary formulas of probability logic to correlations between quantifier-free formulas. We completely classify binary correlations in terms of the Kim-Pillay space, leading to strong results on the interpretative power of pure probability logic over a binary language. Assuming higher amalgamation of independent types, we prove a higher stationarity statement for such correlations. We also give a short model-theoretic proof of a categoricity theorem for continuous logic structures with a measure of full support, generalizing theorems of Gromov-Vershik and Keisler, and often providing a canonical…
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Taxonomy
TopicsAdvanced Algebra and Logic
