31 citations · 45 across the 7 of their papers we have counts for
4 papers · 1 filter
Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages
Daniel Lundén, Johannes Borgström, David Broman
Probabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic progra…
Deriving Probability Density Functions from Probabilistic Functional Programs
Sooraj Bhat, Johannes Borgström, Andrew D. Gordon +1
The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods.…
A Lambda-Calculus Foundation for Universal Probabilistic Programming
Johannes Borgström, Ugo Dal Lago, Andrew D. Gordon +1
We develop the operational semantics of an untyped probabilistic lambda-calculus with continuous distributions, as a foundation for universal probabilistic programming languages su…
Proceedings Combined 20th International Workshop on Expressiveness in Concurrency and 10th Workshop on Structural Operational Semantics
Johannes Borgström, Bas Luttik
This volume contains the proceedings of the Combined 20th International Workshop on Expressiveness in Concurrency and the 10th Workshop on Structural Operational Semantics (EXPRESS…