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+### Probabilistic Programming @ IPA-Lab
+
+Probabilistic programming provides an intuitive means to specify Bayesian models as programs and to automatically perform posterior inference with general-purpose algorithms.
+This makes probabilistic modelling more accessible to non-experts in Bayesian statictics, however, it comes with new challenges.
+Through the lens of software engineering, we explore new approaches to improve the probabilistic programming experience.
+
+**Highlighted Publications:**
+
+**[Online and Interactive Bayesian Inference Debugging](https://arxiv.org/abs/2510.26579)**  
+*Nathanel Nussbaumer, Markus Böck, and Jürgen Cito. To appear in ICSE 2026.*  
+Introduces a new approach for debugging probabilistic programs, where diagnostic tools evaluate the quality of inference during execution.
+We implemented a debugger as a VSCode extension and showed that our approach significantly reduces time and difficulty on inference debugging tasks in a user-study.
+
+**[Language-Agnostic Static Analysis of Probabilistic Programs](https://dl.acm.org/doi/10.1145/3691620.3695031)**  
+*Markus Böck, Michael Schröder, and Jürgen Cito. ASE 2024.*  
+Describes a framework which allows the formulation of program analyses which target problems specific to the probabilistic programming environment in a high-level API.
+The analyses can be applied to any probabilistic programming language for which light-weight bindings are implemented.
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