45 citations · 292 across the 21 of their papers we have counts for
3 papers · 1 filter
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…
Automated learning with a probabilistic programming language: Birch
Lawrence M. Murray, Thomas B. Schön
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Tur…
Improving the particle filter in high dimensions using conjugate artificial process noise
Anna Wigren, Lawrence Murray, Fredrik Lindsten
The particle filter is one of the most successful methods for state inference and identification of general non-linear and non-Gaussian models. However, standard particle filters s…