7 citations · 7 across the 1 of their papers we have counts for
4 papers
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…
Modal Logics for Nominal Transition Systems
Joachim Parrow, Johannes Borgström, Lars-Henrik Eriksson +2
We define a general notion of transition system where states and action labels can be from arbitrary nominal sets, actions may bind names, and state predicates from an arbitrary lo…
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…