13 citations · 28 across the 4 of their papers we have counts for
4 papers
Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference
Daniel Lundén, Joey Öhman, Jan Kudlicka +3
Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problem…
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…
Automatic Alignment of Sequential Monte Carlo Inference in Higher-Order Probabilistic Programs
Daniel Lundén, David Broman, Fredrik Ronquist +1
Probabilistic programming is a programming paradigm for expressing flexible probabilistic models. Implementations of probabilistic programming languages employ a variety of inferen…
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Lawrence M. Murray, Daniel Lundén, Jan Kudlicka +2
We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce vari…