Lawrence Murray

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Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
Pierre E. JacobESSEC Business SchoolVerified email at essec.edu
Anthony LeeSchool of Mathematics, University of BristolVerified email at bristol.ac.uk
Jan KudlickaAssociate Professor of Data Science, BI Norwegian Business SchoolVerified email at bi.no
David BromanProfessor, KTH Royal Institute of TechnologyVerified email at kth.se
Daniel LundénOracleVerified email at oracle.com
Fredrik RonquistProfessor, Swedish Museum of Natural History, Dept. Bioinformatics and GeneticsVerified email at nrm.se
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Noel CressieDistinguished Professor, University of WollongongVerified email at uow.edu.au
Pierre Del MoralResearch Director, INRIA Bordeaux - Sud OuestVerified email at inria.fr
Sebastian FunkProfessor, London School of Hygiene & Tropical MedicineVerified email at lshtm.ac.uk
Chris HolmesVerified email at stats.ox.ac.uk
nicolas lartillotResearch Director, Université de Lyon 1Verified email at univ-lyon1.fr
Viktor SenderovSwedish Museum for Natural HistoryVerified email at nrm.se
Anna WigrenPhD student, Uppsala UniversityVerified email at it.uu.se
Johannes BorgströmAssociate professor in computing science, Uppsala UniversityVerified email at it.uu.se
Adam KucharskiProfessor, London School of Hygiene & Tropical MedicineVerified email at lshtm.ac.uk
Farhan RizwiSoftware Engineer, Oceans and Atmosphere, CSIROVerified email at csiro.au
Peter OkeCSIROVerified email at csiro.au
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr

Lawrence Murray

Uber AI

Verified email at uber.com - Homepage

Bayesian statistics computational statistics machine learning probabilistic programming high performance computing


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Parallel resampling in the particle filter LM Murray, A Lee, PE Jacob Journal of Computational and Graphical Statistics 25 (3), 789-805, 2016	208	2016
Bayesian state-space modelling on high-performance hardware using LibBi LM Murray Journal of Statistical Software 67, 1--36, 2015	139	2015
Better together? Statistical learning in models made of modules PE Jacob, LM Murray, CC Holmes, CP Robert arXiv preprint arXiv:1708.08719, 2017	109*	2017
Comparative analysis of dengue and Zika outbreaks reveals differences by setting and virus S Funk, AJ Kucharski, A Camacho, RM Eggo, L Yakob, LM Murray, ... PLoS Neglected Tropical Diseases 10 (12), e0005173, 2016	108	2016
GPU acceleration of Runge-Kutta integrators L Murray Parallel and Distributed Systems, IEEE Transactions on 23, 94-101, 2012	90	2012
Automated learning with a probabilistic programming language: Birch LM Murray, TB Schön Annual Reviews in Control 46, 29-43, 2018	76	2018
Path storage in the particle filter PE Jacob, LM Murray, S Rubenthaler Statistics and Computing 25, 487-496, 2015	71	2015
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs LM Murray, D Lundén, J Kudlicka, D Broman, TB Schön Proceedings of Machine Learning Research (AIStats) 84, 1037--1046, 2018	68	2018
Sequential Monte Carlo with highly informative observations P Del Moral, LM Murray SIAM/ASA Journal on Uncertainty Quantification 3 (1), 969-997, 2015	58	2015
Bayesian learning and predictability in a stochastic nonlinear dynamical model J Parslow, N Cressie, EP Campbell, E Jones, L Murray Ecological Applications 23 (4), 679-698, 2013	53	2013
A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model E Jones, J Parslow, L Murray Australian Meteorological and Oceanographic Journal 59 (SP), 7-16, 2010	53	2010
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), 244, 2021	49	2021
GPU acceleration of the particle filter: the Metropolis resampler L Murray arXiv preprint arXiv:1202.6163, 2012	49	2012
On disturbance state-space models and the particle marginal Metropolis-Hastings sampler LM Murray, EM Jones, J Parslow SIAM/ASA Journal on Uncertainty Quantification 1 (1), 494-521, 2013	45*	2013
Assimilation of glider and mooring data into a coastal ocean model EM Jones, PR Oke, F Rizwi, LM Murray Ocean Modelling 47, 1-13, 2012	45	2012
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo TB Schön, A Svensson, L Murray, F Lindsten Mechanical systems and signal processing 104, 866-883, 2018	43	2018
Continuous time particle filtering for fMRI L Murray, AJ Storkey Advances in Neural Information Processing Systems (NIPS), 1049-1056, 2008	33	2008
Particle smoothing in continuous time: A fast approach via density estimation L Murray, A Storkey IEEE Transactions on Signal Processing 59 (3), 1017-1026, 2010	30	2010
Distributed Markov chain Monte Carlo L Murray NIPS workshop on learning on cores, clusters and clouds, 2010	29	2010
High-performance pseudo-random number generation on graphics processing units N Nandapalan, RP Brent, LM Murray, AP Rendell Parallel Processing and Applied Mathematics: 9th International Conference …, 2012	28	2012

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