Research

Lawrence Murray

Lawrence Murray is a research scientist in computational statistics at CSIRO Mathematics, Informatics and Statistics in Perth, Australia. He received his PhD in informatics, specialising in machine learning, at the School of Informatics of the University of Edinburgh, and honours degree in software engineering at the Australian National University. His research interests include:

  • Bayesian inference, particularly sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC) and particle Markov chain Monte Carlo (PMCMC) methods.
  • High performance computing on graphics processing units (GPUs) and distributed memory clusters.
  • Nonlinear state-space models.
  • Continuous-time state-space models using ordinary or stochastic differential equations.
  • Applications of the above in environmental science.

Contact lawrence.murray@csiro.au


Related posts

LibBi released

Sunday, June 23rd, 2013

After four years of work, I’m very happy to announce that LibBi is now available as open source software. LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters. The staple methods of LibBi are based on sequential Monte Carlo (SMC), [...]

New papers on arXiv.org

Wednesday, February 29th, 2012

I’ve added one preprint and one older workshop paper to arXiv.org, given recent interest, see below. Murray, L. M.; Jones, E. M. & Parslow, J. (2012). On collapsed state-space models and the particle marginal Metropolis-Hastings sampler. In review. [arXiv] Murray, L.M. (2011). GPU acceleration of the particle filter: The Metropolis resampler. Distributed machine learning and [...]

Bayesian Learning of Continuous-Time Dynamical Systems

Saturday, June 27th, 2009

I’ve posted the final version of my PhD thesis, "Bayesian Learning of Continuous-Time Dynamical Systems, with Applications in Functional Magnetic Resonance Imaging" to the research page. Now assessed, corrected and passed! Note that this may serve as a useful manual for some of the detail behind the algorithms of the dysii Dynamic Systems Library.

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