Publications by Daniel Lundén
F. Ronquist et al., "Universal probabilistic programming offers a powerful approach to statistical phylogenetics," Communications Biology, vol. 4, no. 1, 2021.
D. Lundén et al., "Automatic Alignment in Higher-Order Probabilistic Programming Languages," in Programming Languages and Systems, 2023.
D. Lundén et al., "Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference," in Programming Languages and Systems : 31st European Symposium on Programming, ESOP 2022, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Munich, Germany, April 2–7, 2022, Proceedings, 2022, pp. 29-56.
D. Lundén, J. Borgström and D. Broman, "Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages," in Programming Languages and Systems, 2021, pp. 404-431.
L. Murray et al., "Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs," in Proceeding of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), 2018.
D. Lundén, "Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:22, 2023.
D. Lundén et al., "Suspension Analysis and Selective Continuation-Passing Style for Higher-Order Probabilistic Programming Languages," (Manuscript).
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