Lawrence Murray
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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
Bayesian state-space modelling on high-performance hardware using LibBi
LM Murray
Journal of Statistical Software 67, 1--36, 2015
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
Better together? Statistical learning in models made of modules
PE Jacob, LM Murray, CC Holmes, CP Robert
arXiv preprint arXiv:1708.08719, 2017
GPU acceleration of Runge-Kutta integrators
L Murray
Parallel and Distributed Systems, IEEE Transactions on 23, 94-101, 2012
Automated learning with a probabilistic programming language: Birch
LM Murray, TB Schön
Annual Reviews in Control 46, 29-43, 2018
Path storage in the particle filter
PE Jacob, LM Murray, S Rubenthaler
Statistics and Computing 25, 487-496, 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
Sequential Monte Carlo with highly informative observations
P Del Moral, LM Murray
SIAM/ASA Journal on Uncertainty Quantification 3 (1), 969-997, 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
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
GPU acceleration of the particle filter: the Metropolis resampler
L Murray
arXiv preprint arXiv:1202.6163, 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
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
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
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
Continuous time particle filtering for fMRI
L Murray, AJ Storkey
Advances in Neural Information Processing Systems (NIPS), 1049-1056, 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
Distributed Markov chain Monte Carlo
L Murray
NIPS workshop on learning on cores, clusters and clouds, 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
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