Lawrence Murray

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	All	Since 2016
Citations	976	672
h-index	18	15
i10-index	24	20

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20092010201120122013201420152016201720182019202020213 8 15 39 71 68 88 101 108 137 142 179 3

Co-authors

Pierre E. JacobAssociate Professor, Department of Statistics, Harvard UniversityVerified email at fas.harvard.edu
Thomas SchönProfessor, Uppsala UniversityVerified email at it.uu.se
Anthony LeeSchool of Mathematics, University of BristolVerified email at bristol.ac.uk
David BromanKTH Royal Institute of TechnologyVerified email at kth.se
Daniel LundénPhD student, KTH Royal Institute of TechnologyVerified email at kth.se
Jan KudlickaUppsala UniversityVerified email at it.uu.se
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Fredrik LindstenAssociate Professor, Linköping UniversityVerified email at liu.se
Pierre Del MoralResearch Director, INRIA Bordeaux - Sud OuestVerified email at inria.fr
Sebastian FunkAssociate Professor, London School of Hygiene & Tropical MedicineVerified email at lshtm.ac.uk
Chris HolmesVerified email at stats.ox.ac.uk
Noel CressieDistinguished Professor, University of WollongongVerified email at uow.edu.au
Sylvain RubenthalerUniversité Nice Sophia Antipolis, laboratoire J. A. DieudonnéVerified email at unice.fr
Fredrik RonquistProfessor, Swedish Museum of Natural History, Dept. Bioinformatics and GeneticsVerified email at nrm.se
Farhan RizwiSoftware Engineer, Oceans and Atmosphere, CSIROVerified email at csiro.au
Peter OkeCSIROVerified email at csiro.au
Adam KucharskiAssociate Professor, London School of Hygiene & Tropical MedicineVerified email at lshtm.ac.uk
Anna WigrenPhD student, Uppsala UniversityVerified email at it.uu.se
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr
Richard P BrentAustralian National University; University of Newcastle, AustraliaVerified email at anu.edu.au

Lawrence Murray

Uber AI

Verified email at uber.com - Homepage

Bayesian statistics computational statistics machine learning probabilistic programming high performance computing


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Parallel resampling in the particle filter LM Murray, A Lee, PE Jacob Journal of Computational and Graphical Statistics 25 (3), 789-805, 2016	114*	2016
Bayesian state-space modelling on high-performance hardware using LibBi LM Murray Journal of Statistical Software 67, 1--36, 2015	95	2015
GPU acceleration of Runge-Kutta integrators L Murray Parallel and Distributed Systems, IEEE Transactions on 23, 94-101, 2012	72	2012
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	66	2016
Path storage in the particle filter PE Jacob, LM Murray, S Rubenthaler Statistics and Computing 25 (2), 487-496, 2015	52	2015
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	49	2010
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	44	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	42	2012
GPU acceleration of the particle filter: the Metropolis resampler L Murray arXiv preprint arXiv:1202.6163, 2012	39	2012
Sequential Monte Carlo with highly informative observations P Del Moral, LM Murray SIAM/ASA Journal on Uncertainty Quantification 3 (1), 969-997, 2015	38	2015
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	38*	2013
Better together? Statistical learning in models made of modules PE Jacob, LM Murray, CC Holmes, CP Robert arXiv preprint arXiv:1708.08719, 2017	37*	2017
Continuous time particle filtering for fMRI L Murray, AJ Storkey Advances in Neural Information Processing Systems (NIPS), 1049-1056, 2008	32	2008
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	30	2018
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	28	2010
High-performance pseudo-random number generation on graphics processing units N Nandapalan, RP Brent, LM Murray, AP Rendell International Conference on Parallel Processing and Applied Mathematics, 609-618, 2011	26	2011
Automated learning with a probabilistic programming language: Birch LM Murray, TB Schön Annual Reviews in Control 46, 29-43, 2018	25	2018
Distributed Markov chain Monte Carlo L Murray NIPS workshop on learning on cores, clusters and clouds, 2010	24	2010
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	18	2018
Learning structural equation models for fMRI E Simonotto, H Whalley, S Lawrie, L Murray, D Mcgonigle, AJ Storkey Advances in Neural Information Processing Systems (NIPS), 1329-1336, 2007	17	2007

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