Asymptotic equivalence of probability measures and stochastic processes

Abstract

Let $P_n$ and $Q_n$ be two probability measures representing two different probabilistic models of some system (e.g., an $n$-particle equilibrium system, a set of random graphs with $n$ vertices, or a stochastic process evolving over a time $n$) and let $M_n$ be a random variable representing a 'macrostate' or 'global observable' of that system. We provide sufficient conditions, based on the Radon-Nikodym derivative of $P_n$ and $Q_n$, for the set of typical values of $M_n$ obtained relative to $P_n$ to be the same as the set of typical values obtained relative to $Q_n$ in the limit $n\to \infty$. This extends to general probability measures and stochastic processes the well-known thermodynamic-limit equivalence of the microcanonical and canonical ensembles, related mathematically to the asymptotic equivalence of conditional and exponentially-tilted measures. In this more general sense, two probability measures that are asymptotically equivalent predict the same typical or macroscopic properties of the system they are meant to model.

v1: 16 pages. v2: 17 pages, precisions, examples and references added. v3: Minor typos corrected. Close to published version

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Cited by in corpus (4)

• Current large deviations for partially asymmetric particle systems on a ring
• Current fluctuations in a semi-infinite line
• Efficient large deviation estimation based on importance sampling
• Ensemble inequivalence and phase transitions in unlabeled networks