FRAPPANT – Formal Reasoning About Probabilistic Programs: Breaking New Ground for Automation

FRAPPANT’s objective is to enable predictable probabilistic programming by developing unique, advanced formal verification techniques.

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ERC Advanced Grant 2017 (787914). Project duration: 1.11.2018 to 31.10.2024. See also the CORDIS fact sheet on FRAPPANT and the RWTH press release.

Researchers. Joost-Pieter Katoen, Kevin Batz, Lutz Klinkenberg, Bahare Salmani, Philipp Schroer.


Probabilistic programs describe recipes on how to infer statistical conclusions about data from a complex mixture of uncertain data and real-world observations. They can represent probabilistic graphical models far beyond the capabilities of Bayesian networks and are expected to have a major impact on machine intelligence.

Probabilistic programs are ubiquitous. They steer autonomous robots and self-driving cars, are key to describe security mechanisms, naturally code up randomised algorithms for solving NP-hard problems, and are rapidly encroaching AI. Probabilistic programming aims to make probabilistic modeling and machine learning accessible to the programmer.

Probabilistic programs, though typically relatively small in size, are hard to grasp, let alone automatically checkable. Are they doing the right thing? What’s their precision? These questions are notoriously hard — even the most elementary question “does a program halt with probability one?” is “more undecidable” than the halting problem — and can (if at all) be answered with statistical evidence only. Bugs thus easily occur. Hard guarantees are called for. The objective of this project is to enable predictable probabilistic programming. We do so by developing formal verification techniques.

Whereas program correctness is pivotal in computer science, the formal verification of probabilistic programs is in its infancy. The project aims to fill this barren landscape by developing program analysis techniques, leveraging model checking, deductive verification, and static analysis. Challenging problems such as checking program equivalence, loop-invariant and parameter synthesis, program repair, program robustness and exact inference using weakest precondition reasoning will be tackled. The techniques will be evaluated in the context of probabilistic graphical models, randomised algorithms, and autonomous robots.

FRAPPANT will spearhead formally verifiable probabilistic programming.

Thesis Projects, Lectures, and Seminars

If you are interested in working on this project as a thesis or Hiwi student, contact one of our researchers on the project. We always have interesting topics available related to this project.

We also provide a recurring lecture series on probabilistic programs. For example, check out the WS 2022/23 lecture page. Furthermore, our seminars regularly deal with topics related to FRAPPANT. Head over to our teaching page for more information.

Our Publications Related to FRAPPANT

DOI [bibtex]
title = {Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants},
author = {Kevin Batz and Mingshuai Chen and Sebastian Junges and Benjamin Lucien Kaminski and Joost-Pieter Katoen and Christoph Matheja},
booktitle = {LNCS},
volume = {13994},
type = {Conference Paper},
year = {2023},
doi = {10.1007/978-3-031-30820-8_25},
url = {},
Kevin Batz, Mingshuai Chen, Sebastian Junges, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja. Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants, Tools and Algorithms for the Construction and Analysis of Systems, Volume 13994 of LNCS, 2023.
fulltext PDF [bibtex]
title = {Exact Probabilistic Inference Using Generating Functions},
author = {Lutz Klinkenberg and Tobias Winkler and Mingshuai Chen and Joost-Pieter Katoen},
type = {Preprint},
year = {2023},
url = {},
Lutz Klinkenberg, Tobias Winkler, Mingshuai Chen, Joost-Pieter Katoen. Exact Probabilistic Inference Using Generating Functions, 2023.
DOI [bibtex]
title = {Encoding inductive invariants as barrier certificates: Synthesis via difference-of-convex programming},
author = {Qiuye Wang and Mingshuai Chen and Bai Xue and Naijun Zhan and Joost-Pieter Katoen},
publisher = {Elsevier},
journal = {Information and computation},
volume = {289(A)},
pages = {pages 104965},
type = {Journal Article},
year = {2022},
doi = {10.1016/j.ic.2022.104965},
url = {},
Qiuye Wang, Mingshuai Chen, Bai Xue, Naijun Zhan, Joost-Pieter Katoen. Encoding inductive invariants as barrier certificates: Synthesis via difference-of-convex programming, Information and computation 289 (A), pages 104965, Elsevier, 2022.
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