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	总计	2019 年至今
引用	152	141
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i10 指数	5	5

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David BromanProfessor, KTH Royal Institute of Technology在 kth.se 的电子邮件经过验证
Daniel LundénOracle在 oracle.com 的电子邮件经过验证
Lawrence MurrayUber AI在 uber.com 的电子邮件经过验证
Fredrik RonquistProfessor, Swedish Museum of Natural History, Dept. Bioinformatics and Genetics在 nrm.se 的电子邮件经过验证
Thomas SchönProfessor, Uppsala University在 it.uu.se 的电子邮件经过验证
Viktor SenderovSwedish Museum for Natural History在 nrm.se 的电子邮件经过验证
nicolas lartillotResearch Director, Université de Lyon 1在 univ-lyon1.fr 的电子邮件经过验证
Johannes BorgströmAssociate professor in computing science, Uppsala University在 it.uu.se 的电子邮件经过验证
Emma GranqvistResearcher at the Department of Bioinformatics and Genetics, Swedish Museum of Natural History在 nrm.se 的电子邮件经过验证
Joey ÖhmanEmbark Studios在 embark-studios.com 的电子邮件经过验证
Anders F. AnderssonProfessor in Metagenomics, Science for Life Laboratory, KTH Royal Institute of Technology在 scilifelab.se 的电子邮件经过验证
Ela Iwaszkiewicz-EggebrechtDept. Bioinformatics and Genetics, Swedish Museum of Natural History在 nrm.se 的电子邮件经过验证
Piotr ŁukasikAssociate Professor at Jagiellonian University, Kraków, Poland在 uj.edu.pl 的电子邮件经过验证
Tomas RoslinSwedish University of Agricultural Sciences在 slu.se 的电子邮件经过验证
Ayco JM TackUniversity of Stockholm在 su.se 的电子邮件经过验证
Andreia MiraldoThe Swedish Museum of Natural History在 nrm.se 的电子邮件经过验证
Mateusz BuczekJagiellonian University在 uj.edu.pl 的电子邮件经过验证
Lars HummelgrenPhD student, KTH Royal Institute of Technology在 kth.se 的电子邮件经过验证
Fredrik LindstenAssociate Professor, Linköping University在 liu.se 的电子邮件经过验证
Viktor PalmkvistPhD Student, KTH Royal Institute of Technology在 kth.se 的电子邮件经过验证

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Jan Kudlicka

Associate Professor of Data Science, BI Norwegian Business School

在 bi.no 的电子邮件经过验证 - 首页

Probabilistic programming Monte Carlo inference


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Delayed sampling and automatic Rao-Blackwellization of probabilistic programs L Murray, D Lundén, J Kudlicka, D Broman, T Schön International Conference on Artificial Intelligence and Statistics, 1037-1046, 2018	66	2018
Universal probabilistic programming offers a powerful approach to statistical phylogenetics F Ronquist, J Kudlicka, V Senderov, J Borgström, N Lartillot, D Lundén, ... Communications Biology 4 (1), 2021	42	2021
Optimizing insect metabarcoding using replicated mock communities E Iwaszkiewicz‐Eggebrecht, E Granqvist, M Buczek, M Prus, J Kudlicka, ... Methods in ecology and evolution 14 (4), 1130-1146, 2023	15	2023
Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. D Lundén, J Öhman, J Kudlicka, V Senderov, F Ronquist, D Broman ESOP, 29-56, 2022	10	2022
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling J Kudlicka, LM Murray, F Ronquist, TB Schön Uncertainty in Artificial Intelligence, 679-689, 2020	10	2020
Probabilistic programming: a powerful new approach to statistical phylogenetics F Ronquist, J Kudlicka, V Senderov, J Borgström, N Lartillot, D Lundén, ...	3	2020
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs.(2017) LM Murray, D Lundén, J Kudlicka, D Broman, TB Schön arXiv preprint arXiv:1708.07787, 2017	3	2017
Probabilistic Programming for Birth-Death Models of Evolution J Kudlicka Acta Universitatis Upsaliensis, 2021	2	2021
Particle filter with rejection control and unbiased estimator of the marginal likelihood J Kudlicka, LM Murray, TB Schön, F Lindsten ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020	1	2020
Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages D Lundén, L Hummelgren, J Kudlicka, O Eriksson, D Broman European Symposium on Programming, 302-330, 2024		2024
Suspension Analysis and Selective Continuation-Passing Style for Higher-Order Probabilistic Programming Languages D Lundén, L Hummelgren, J Kudlicka, O Eriksson, D Broman arXiv preprint arXiv:2302.13051, 2023		2023
TreePPL: A Universal Probabilistic Programming Language for Phylogenetics VE Senderov, J Kudlicka, D Lunden, V Palmkvist, MP Braga, E Granqvist, ... bioRxiv, 2023.10. 10.561673, 2023		2023
Universal Probabilistic Programming Language Compilation with Parallel Efficient Sequential Monte Carlo Inference D Lundén, J Öhman, J Kudlicka, V Senderov, F Ronquist, D Broman arXiv preprint arXiv:2112.00364, 2021		2021
Publisher Correction: Universal probabilistic programming offers a powerful approach to statistical phylogenetics F Ronquist, J Kudlicka, V Senderov, J Borgström, N Lartillot, D Lundén, ... Communications Biology 4, 2021		2021

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