13 citations · 32 across the 6 of their papers we have counts for
6 papers
Automatic Alignment in Higher-Order Probabilistic Programming Languages
Daniel Lundén, Gizem Çaylak, Fredrik Ronquist +1
Probabilistic Programming Languages (PPLs) allow users to encode statistical inference problems and automatically apply an inference algorithm to solve them. Popular inference algo…
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…
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…
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…
Bayesian Analysis of Partitioned Data
Brian R. Moore, Jim McGuire, Fredrik Ronquist +1
Variation in the evolutionary process across the sites of nucleotide sequence alignments is well established, and is an increasingly pervasive feature of datasets composed of gene…
Probabilistic Graphical Model Representation in Phylogenetics
Sebastian Höhna, Tracy A. Heath, Bastien Boussau +3
Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and s…