# Relational reasoning via probabilistic coupling

arXiv: https://arxiv.org/abs/1509.03476 · HTML: https://mlsanity.com/paper/1509.03476/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2015.

## Built on by (most cited first, 2 of 2)

- [Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism](https://mlsanity.com/paper/2201.07474/) (2022)
- [GradInf: Gradient Estimation as Probabilistic Inference](https://mlsanity.com/paper/2607.07840/) (2026)

## Abstract (arXiv)

Probabilistic coupling is a powerful tool for analyzing pairs of probabilistic processes. Roughly, coupling two processes requires finding an appropriate witness process that models both processes in the same probability space. Couplings are powerful tools proving properties about the relation between two processes, include reasoning about convergence of distributions and stochastic dominance---a probabilistic version of a monotonicity property. While the mathematical definition of coupling looks rather complex and cumbersome to manipulate, we show that the relational program logic pRHL---the logic underlying the EasyCrypt cryptographic proof assistant---already internalizes a generalization of probabilistic coupling. With this insight, constructing couplings is no harder than constructing logical proofs. We demonstrate how to express and verify classic examples of couplings in pRHL, and we mechanically verify several couplings in EasyCrypt.