David J. Warne

发表

Acceleration of expensive computations in Bayesian statistics using vector operations pdf

Christopher C. Drovandi, Scott A. Sisson, David J. Warne, 2019, ArXiv.

Developments in texture-based vector field visualisation techniques

Neil A. Kelson, David J. Warne, Joseph A. Young, 2010 .

Computational inference without proposal kernels

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2017 .

Using Experimental Data and Information Criteria to Guide Model Selection for Reaction–Diffusion Problems in Mathematical Biology

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2018, Bulletin of Mathematical Biology.

Multilevel Monte Carlo for likelihood-free Bayesian inference of rate parameters for stochastic models of biochemical reactions

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2017 .

Supplementary material from "Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art"

Ruth E. Baker, David J. Warne, Matthew J. Simpson, 2019 .

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Jasmine Banks, Neil A. Kelson, Ross F. Hayward, 2014, HiPC 2014.

Accelerating Bayesian inference for stochastic models by preconditioning with approximate continuum limit descriptions

Matthew J. Simpson, David J. Warne, 2019 .

Vector Operations for Accelerating Expensive Bayesian Computations – A Tutorial Guide

Scott A. Sisson, David J. Warne, Christopher Drovandi Queensland University of Technology, 2019, Bayesian Analysis.

Practical introduction to development of sage applications

David J. Warne, Timothy Gurnett, 2010, HiPC 2010.

Comparison of High Level FPGA Hardware Design for Solving Tri-diagonal Linear Systems

Ross Hayward, David J. Warne, Neil Kelson, 2014, ICCS.

Accelerating computational Bayesian inference for stochastic biochemical reaction network models using multilevel Monte Carlo sampling

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2016, bioRxiv.

Computationally efficient inference methods for biochemical reaction networks

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2016 .

Experiences with implementing common mathematical operations using field programmable gate arrays

Neil A. Kelson, David J. Warne, Ulrich Rueckert, 2012, HiPC 2012.

A heterogeneous computing approach to maximum likelihood parameter estimation for the Heston model of stochastic volatility

Stan Hurn, David J. Warne, Kenneth A. Lindsay, 2016, HiPC 2016.

CTCombine : rotating and combining CT data with an accurate detector model to simulate radiotherapy portal imaging at non-zero beam angles

Tanya Kairn, Jamie Trapp, David J. Warne, 2009, HiPC 2009.

Bayesian inference and information criteria for selection of reaction-diffusion models of collective cell behaviour

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2019 .

Rapid Bayesian inference for expensive stochastic models

Ruth E. Baker, University of Oxford, David J. Warne, 2019 .

Solving Tri-Diagonal Linear Systems using Field Programmable Gate Arrays

Neil A. Kelson, Ross F. Hayward, David J. Warne, 2012, HiPC 2012.

The Dynamics of Cellular Automata on 2-Manifolds is Affected by Topology

Ross Hayward, David J. Warne, 2015, J. Cell. Autom..

An Efficient Algorithm for the Detection of Eden

Ross Hayward, David J. Warne, Neil Kelson, 2013, Complex Syst..

Multilevel Monte Carlo for simulation and inference of biochemical reaction networks

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2019 .

Image-based flow visualisation (IBFV) to enhance interpretation of complex flow patterns within a shallow tidal barrier estuary

David J. Warne, Genevieve Larsen, Joseph Young, 2013, Environ. Model. Softw..

Accurately simulating the production of radiotherapy portal images using non-zero beam angles

Tanya Kairn, David J. Warne, Mark Dwyer, 2011, HiPC 2011.

Computational inference in mathematical biology: Methodological developments and applications

David J. Warne, 2020 .

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

David J. Warne, Kenneth A. Lindsay, 2015, HiPC 2015.

On the effect of topology on cellular automata rule spaces

David J. Warne, 2013, HiPC 2013.

Visualisation of complex flows using texture-based techniques

Neil A. Kelson, David J. Warne, Joseph A. Young, 2013, HiPC 2013.

Utility of genetic algorithms for solving large scale construction time/cost trade-off problems

Forrest J. Masters, David J. Warne, Duzgun Agdas, 2018 .

Multilevel rejection sampling for approximate Bayesian computation

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2017, Comput. Stat. Data Anal..

Visualisation of groundwater flow using texture based visualisation techniques

David J. Warne, Joseph A. Young, Benjamin Donald Cumming, 2009, HiPC 2009.

Calibration of treatment time intensity modulated EPID images for anatomical imaging

David J. Warne, Moh'd Al-Roumi, Katrina Biggerstaff, 2013, HiPC 2013.

Multifidelity Multilevel Monte Carlo to Accelerate Approximate Bayesian Parameter Inference for Partially Observed Stochastic Processes

University of Oxford, David J. Warne, Thomas P. Prescott, 2021, SSRN Electronic Journal.

Rapid Bayesian Inference for Expensive Stochastic Models

Ruth E. Baker, Matthew J. Simpson, David J. Warne, 2021, Journal of Computational and Graphical Statistics.

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

David J. Warne, K. Mengersen, A. Mira, 2020, BMC Public Health.

Implementation of parallel tridiagonal solvers for a heterogeneous computing environment

Jasmine Banks, Neil A. Kelson, David J. Warne, 2016 .

Explainability of the COVID-19 epidemiological model with nonnegative tensor factorization

David J. Warne, K. Mengersen, R. Nayak, 2022, International journal of data science and analytics.

An Eﬃcient Algorithm for the Detection of Eden

David J. Warne, D. Warne, 2017 .

Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes

David J. Warne, Thomas P. Prescott, Alan Turing Institute, 2021, J. Comput. Phys..

Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth

David J. Warne, Oliver J. Maclaren, R. J. Murphy, 2022, bioRxiv.

Rapid Bayesian Inference for Expensive Stochastic Models

David J. Warne, R. Baker, M. Simpson, 2019, J. Comput. Graph. Stat..