13 citations · 24 across the 5 of their papers we have counts for
5 papers
Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages
Daniel Lundén, Lars Hummelgren, Jan Kudlicka +2
Universal probabilistic programming languages (PPLs) make it relatively easy to encode and automatically solve statistical inference problems. To solve inference problems, PPL impl…
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…
Particle filter with rejection control and unbiased estimator of the marginal likelihood
Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön +1
We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties o…
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist +1
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle fi…
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…