Information on trajectories: martingales and random times

Abstract:Accounting for information flow on the path space of trajectories of a nonnegative martingale yields exact variational identities for it, even at arbitrary random times. This recovers the widely used classical concentration inequalities, from Ville to PAC-Bayes, and measures what each one discards. The tail a bound controls is itself a relative entropy, resolved by the chain rule into per-step conditional divergences. The discarded slack has an exact form in each of three geometries: a Gibbs tilt for the Azuma-Hoeffding and PAC-Bayes bounds, the crossing itself for Ville's and for pooled tests, and a dominating certificate for the $L^p$ maximal bound. That certificate's optional-stopping deficit resolves per step into Bregman divergences of the running maximum. On a path-time space, the same identity gains one factor that prices anticipation: an arbitrary random time carries an e-process ``peeking penalty.'' The partition function can be read as a coalescent--a prefix-sharing probability of independent copies--and geometric mixtures of test martingales gain a pooling benefit for multi-model safe testing.

Probability (math.PR); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)

arXiv:2608.20337 [math.PR]

(or arXiv:2608.20337v1 [math.PR] for this version)

https://doi.org/10.48550/arXiv.2608.20337 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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