Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-29T08:49:12.852Z Has data issue: false hasContentIssue false

5 - Probabilistic Couplings from Program Logics

Published online by Cambridge University Press:  18 November 2020

Gilles Barthe
Affiliation:
Max Planck Institute for Security and Privacy
Joost-Pieter Katoen
Affiliation:
RWTH Aachen University, Germany
Alexandra Silva
Affiliation:
University College London
HTML view is not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Summary

Probabilistic couplings are a powerful abstraction for analysing probabilistic properties. Originating from research in probability theory, a coupling is a distribution over pairs that relates – or couples – two given distributions. If we can find a coupling with certain properties, then we can conclude properties about the two related distributions. In this way, probabilistic relational properties – properties comparing two executions of a probabilistic program – can be established by building a suitable coupling. Couplings have also been explored in the logic and verification literature. For example, probabilistic bisimulation asserts that there exists a coupling; in this way, couplings can be used to verify equivalence of finite state probabilistic transition systems. However, their use in mathematics suggests that couplings can prove more sophisticated properties for richer probabilistic computations, such as imperative programs and infinite state systems. Furthermore, we can borrow a tool from probability theory, called proof by coupling, to construct couplings in a compositional fashion. This chapter describes how coupling proofs can be naturally encoded in pRHL, a relational program logic originally designed for verifying cryptographic protocols. Several examples are presented, showing how to use this proof technique to verify equivalence, stochastic domination and probabilistic convergence.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2020
Creative Commons
Creative Common License - CCCreative Common License - BY
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY 4.0 https://creativecommons.org/cclicenses/

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×