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Lawrence Murray is the developer of the probabilistic programming languages Birch and LibBi, with research interests including computational statistics, machine learning, probabilistic programming, and high-performance computing. He currently works at Uber AI, and has previously worked at Uppsala, Oxford and CSIRO. He holds a Ph.D. (informatics) from Edinburgh and honours degree (software engineering) from ANU.

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Research Probabilistic programming: a powerful new approach to statistical phylogenetics

F. Ronquist, J. Kudlicka, V. Senderov, J. Borgström, N. Lartillot, D. Lundén, L.M. Murray, T.B. Schön, D. Broman

Research Parameter elimination in particle Gibbs sampling

A. Wigren, R.S. Risuleo, L.M. Murray and F. Lindsten

Research Birch

An object-oriented, universal probabilistic programming language.

Research Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

L.M. Murray, D. Lundén, J. Kudlicka, D. Broman, T.B. Schön

Research Better together? Statistical learning in models made of modules

P.E. Jacob, L.M. Murray, C.C. Holmes, C.P. Robert

Research Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

T.B. Schön, A. Svensson, L.M. Murray, and F. Lindsten

Research Anytime Monte Carlo

L.M. Murray, S. Singh, P.E. Jacob and A. Lee

Research Comparative Analysis of Dengue and Zika Outbreaks Reveals Differences by Setting and Virus

S. Funk, A.J. Kucharski, A. Camacho, R.M. Eggo, L. Yakob, L.M. Murray, and W.J. Edmunds

Research Sequential Monte Carlo with Highly Informative Observations

P. Del Moral and L.M. Murray

Research Parallel Resampling in the Particle Filter

L.M. Murray, A. Lee and P.E. Jacob

Research Path Storage in the Particle Filter

P.E. Jacob, L.M. Murray and S. Rubenthaler

Research Rethinking soil carbon modelling: a stochastic approach to quantify uncertainties

D. Clifford, D. Pagendam, J. Baldock, N. Cressie, R. Farquharson, M. Farrell, L. Macdonald and L.M. Murray

Research Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model

J. Parslow, N. Cressie and E.P. Campbell, E. Jones and L.M. Murray

Research LibBi

A high-performance probabilistic programming language for state-space models with GPU and distributed computing support.

Research Feynman-Kac Particle Integration with Geometric Interacting Jumps

P. Del Moral, P.E. Jacob, A. Lee, L.M. Murray and G.W. Peters

Research Assimilation of glider and mooring data into a coastal ocean model

E.M. Jones, P.R. Oke, F. Rizwi and L.M. Murray

Research High-Performance Pseudo-Random Number Generation on Graphics Processing Units

N. Nandapalan, R. Brent, L.M. Murray and A. Rendell

Research Continuous Time Particle Filtering for fMRI

L.M. Murray and A. Storkey

Research Learning structural equation models for fMRI

A.J. Storkey, E. Simonotto, H. Whalley, S. Lawrie, L.M. Murray and D. McGonigle