Probabilistic Call By Push Value

By Editorial Team · September 30, 2026 · 7 min read

Probabilistic Call By Push Value Information Guide

  1. Overview to Probabilistic Call By Push Value
  2. Important Facts
  3. Developments
  4. Detailed Analysis
  5. Summary

Overview to Probabilistic Call By Push Value

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Important Facts

Full [OOPSLA24] Effects and Coeffects in Call-By-Push-Value Update
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Developments

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[HOPE'22] Temporal refinements for Call-By-Push-Value with fixpoint
[HOPE'22] Temporal refinements for Call-By-Push-Value with fixpoint
Christine Tasson: Semantics for Probabilistic Programming
Christine Tasson: Semantics for Probabilistic Programming
[Doctoral Symposium] Towards a Verified Cost Model for Call-by-Push-Value
[Doctoral Symposium] Towards a Verified Cost Model for Call-by-Push-Value
Abstractions in call-by-push-value
Abstractions in call-by-push-value
[POPL 2021] Paradoxes of probabilistic programming (full)
[POPL 2021] Paradoxes of probabilistic programming (full)
[POPL 2021] Relatively Complete Verification of Probabilistic Programs (full)
[POPL 2021] Relatively Complete Verification of Probabilistic Programs (full)
Automatic Reparameterisation of Probabilistic Programs
Automatic Reparameterisation of Probabilistic Programs
CAV 2020 Tutorial: Probabilistic Programming: A Guide for Verificationists
CAV 2020 Tutorial: Probabilistic Programming: A Guide for Verificationists
[OOPSLA'25] Denotational Foundations for Expected Cost Analysis
[OOPSLA'25] Denotational Foundations for Expected Cost Analysis
Semantics of Higher-Order Probabilistic Programs with Conditioning
Semantics of Higher-Order Probabilistic Programs with Conditioning
A Personal Viewpoint on Probabilistic Programming
A Personal Viewpoint on Probabilistic Programming

Detailed Analysis

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Last Updated: September 30, 2026

Summary

[Scheme'26] A Call-by-push-value Scheme Update
For 2026, Probabilistic Call By Push Value remains one of the most talked-about information profiles. Check back for the newest reports.

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Summary

Christine Tasson, Université Paris Diderot simons.berkeley.edu/talks/christine-tasson-12-08-2016 Compositionality. icfp20.sigplan.org/details/hope-2020-papers/8/Variants-of- Lambda-calculus is a fundamental model of computation. It provides a foundation for functional programming. Therefore ... amzn.to/4aLHbLD You're literally one away from a better setup — grab it now! As an Amazon Associate I earn ... Jules Jacobs (Radboud University Nijmegen) Paper: dl.acm.org/doi/pdf/10.1145/3434339 Abstract Kevin Batz (RWTH Aachen University) Benjamin Lucien Kaminski (RWTH Aachen University, Germany) Joost-Pieter Katoen ... Abstract from Maria: Markov chain Monte Carlo (MCMC) algorithms can be used to approximate a Session 1B: CAV 2020 Tutorial B, Chair: Shuvendu Lahiri Speaker: Sriram Sankaranarayanan Denotational Foundations for Expected Cost Analysis (Video, OOPSLA1 2025) Pedro H. Azevedo de Amorim (University of Oxford ... Presented by Fredrik Dahlqvist. Presented at POPL'20. Daniel Roy, University of Toronto simons.berkeley.edu/talks/daniel-roy-10-06-2016 Uncertainty in Computation.

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