Probabilistic Programming offers a concise way to represent stochastic models and perform
automated statistical inference. However, many real-world models have discrete or hybrid …

Due to their quantitative nature, probabilistic programs pose non-trivial challenges for
designing compositional and efficient program analyses. Many analyses for probabilistic …

This article gives an in-depth presentation of the omni-big-step and omni-small-step styles of
semantic judgments. These styles describe operational semantics by relating starting states …

We give a lightweight alternative construction of Jacobs's distributive law for multisets and
distributions that does not involve any combinatorics. We first give a distributive law for lists …

Session types guarantee that message-passing processes adhere to predefined
communication protocols. Prior work on session types has focused on deterministic …

We introduce a version of probabilistic Kleene algebra with angelic nondeterminism and a
corresponding class of automata. Our approach implements semantics via distributions over …

A desired but challenging property of compiler verification is compositionality in the sense
that the compilation correctness of a program can be deduced from that of its substructures …

Although computational complexity is a fundamental aspect of program behavior, it is often
at odds with common type theoretic principles such as function extensionality, which …

Today, a typical project in rigorous reasoning about programming languages begins with an
operational semantics (or maybe several), with proofs of key lemmas proceeding by …

Probabilistic programs are programs that can draw random samples from probability
distributions and involve random control flows. They are becoming increasingly popular and …