• Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference 

      Lundén, Daniel; Öhman, Joey; Kudlicka, Jan; Senderov, Viktor; Ronquist, Fredrik; Broman, David (Chapter, 2022)
      Probabilistic programming languages (PPLs) allow users to encode arbitrary inference problems, and PPL implementations provide general-purpose automatic inference for these problems. However, constructing inference ...
    • Optimizing insect metabarcoding using replicated mock communities 

      Iwaszkiewicz-Eggebrecht, Elzbieta; Granqvist, Emma; Buczek, Mateusz; Prus, Monika; Kudlicka, Jan; Roslin, Tomas; Tack, Ayco J. M.; Andersson, Anders F.; Miraldo, Andreia; Ronquist, Fredrik; Łukasik, Piotr (Peer reviewed; Journal article, 2023)
      Metabarcoding (high-throughput sequencing of marker gene amplicons) has emerged as a promising and cost-effective method for characterizing insect community samples. Yet, the methodology varies greatly among studies and ...