Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 6 - Expected Runtime Analyis by Program Verification

  • Edited by Gilles Barthe, Joost-Pieter Katoen, RWTH Aachen University, Germany, Alexandra Silva, University College London
  • Book:
    Foundations of Probabilistic Programming
    Published online:
    18 November 2020
    Print publication:
    03 December 2020, pp 185-220
    • Chapter
      • You have access
      • Open access
    • PDF
    • Export citation

  • Summary

    This chapter is concerned with analysing the expected runtime of probabilistic programs by exploiting program verification techniques. We introduce a weakest pre-conditioning framework á la Dijkstra that enables to determine the expected runtime in a compositional manner. Like weakest pre-conditions, it is a reasoning framework at the syntax level of programs. Applications of the weakest pre-conditioning framework include determining the expected runtime of randomised algorithms, as well as determining whether a program is positive almost-surely terminating, i.e., whether the expected number of computation steps until termination is finite for every possible input. For Bayesian networks, a restricted class of probabilistic programs, we show that the expected runtime analysis can be fully automated. In this way, the simulation time under rejection sampling can be determined. This is particularly useful for ill-conditioned inference queries.