Skip to the content.

Probabilistic programs are a structured way to describe computations or models with access to some source of randomness. They appear naturally in various safety-, security-, and privacy-critical applications, including randomized algorithms, security protocols, and autonomous systems working in uncertain environments, e.g., due to imprecise sensors.

The behavior of probabilistic programs is often counterintuitive — a consequence of the well-known fact that humans have difficulties reasoning about stochastic processes. In combination with their importance in emerging safety-critical domains, the counterintuitive nature of probabilistic programs means that ensuring their correctness must be based on verification and analysis techniques that are rigorous, tool-supported, and, ideally, largely automated. However, such tools have not kept up with the increasing usage and popularity of probabilistic programs.

In part, the lack of tool support can be explained by the fact that research on probabilistic programs is spread over multiple fields. In addition to the classical understanding of probabilistic programs as randomized algorithms, probabilistic programs have received rapidly increasing attention as a modeling formalism for complex probability distributions in machine learning, artificial intelligence, and cognitive science.

This workshop will provide a forum for research on the automated verification of probabilistic systems that are in some way described by a programming language, with a particular focus on both symbolic methods and compositional approaches.

Invited Speakers


Time Author(s) Title
9:00   Opening
9:10 Jan Hoffmann Sound Probabilistic Inference with Guide Programs
9:50 Steven Holtzen Decomposing Probability Distributions
10:30   Coffee break
11:00 Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný and Đorđe Žikelić Disproving Equivalence of Probabilistic Programs
11:16 Kazuki Watanabe, Sebastian Junges, Jurriaan Rot and Ichiro Hasuo A Unifying Approach to Product Constructions for Quantitative Temporal Inference
11:32 Muqsit Azeem, Jan Křetínský and Maximilian Weininger Sound Value Iteration for Simple Stochastic Games
11:50 Mirco Giacobbe Model checking with Neural Networks
12:30   Lunch break
14:00   Open Problems and Lightning Talks Session
15:30   Closing

Call for Presentations

VeriProP 2024, co-located with CAV, aims to bring together researchers interested in the tool-supported verification of probabilistic programs, models, and systems. This includes probabilistic model checking, program verification in the presence of a source of randomness, or formal guarantees for statistical machine learning algorithms and artificial intelligence systems.

We solicit contributed short presentations. Topics of interest include, but are not limited to:

We call for extended abstracts (1-2 pages in pdf format) describing either ongoing research or an overview of past research in the workshop’s scope. We welcome abstracts covering work that has been previously published or is currently under review. There will be no formal proceedings.


Submission date: May 24th, AoE

Submission link:


The workshop will take place on July 23rd, the day before CAV. Please register via the official CAV website.


The workshop is chaired by:

Previous Editions