Although probabilistic programming is widely used for some restricted classes of statistical
models, existing systems lack the flexibility and efficiency needed for practical use with more …

The Java Virtual Machine (JVM) is the cornerstone of the widely-used Java platform. Thus, it
is critical to ensure the reliability and robustness of popular JVM implementations. However …

This paper introduces SMCP3, a method for automatically implementing custom sequential
Monte Carlo samplers for inference in probabilistic programs. Unlike particle filters and …

As a widely-used platform to support various Java-bytecode-based applications, Java Virtual
Machine (JVM) incurs severe performance loss caused by its real-time program …

Modern probabilistic programming languages aim to formalize and automate key aspects of
probabilistic modeling and inference. Many languages provide constructs for programmable …

We introduce inference metaprogramming for probabilistic programming languages,
including new language constructs, a formalism, and the rst demonstration of e ectiveness in …

Synchronous modeling is at the heart of programming languages like Lustre, Esterel, or
Scade used routinely for implementing safety critical control software, eg, fly-by-wire and …

Probabilistic programming languages (PPLs) allow users to encode arbitrary inference
problems, and PPL implementations provide general-purpose automatic inference for these …

We present Zar: a formally verified compiler pipeline from discrete probabilistic programs
with unbounded loops in the conditional probabilistic guarded command language (cpGCL) …

This paper presents ProbCompCert, a compiler for a subset of the Stan probabilistic
programming language (PPL), in which several key compiler passes have been formally …