We study semantic models of probabilistic programming languages over graphs, and
establish a connection to graphons from graph theory and combinatorics. We show that …

Effectful programs interact in ways that go beyond simple input-output, making
compositional reasoning challenging. Existing work has shown that when such programs …

This paper is about the semantic structures underlying probabilistic programming with
random graphs. Random graphs have applications in statistical modelling across biology …

A UNIFYING SEMANTICS FOR MARKOV KERNELS AND LINEAR OPERATORS Page 1 A
UNIFYING SEMANTICS FOR MARKOV KERNELS AND LINEAR OPERATORS A Dissertation …

There has been much work done in developing semantic structures for interpreting
probabilistic programs. In particular, there have been many models based either on Markov …

In recent years, researchers have proposed various models of linear logic with strong
connections to measure theory, with probabilistic coherence spaces (PCoh) being one of the …

The last decade has seen rapid development in probabilistic semantics, a good portion of
which fall into two categories: those based on monads and those based on linear operators …