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pro vyhledávání: '"GAVAGHAN, DAVID"'
Autor:
Gallagher, Kit, Creswell, Richard, Lambert, Ben, Robinson, Martin, Lei, Chon Lok, Mirams, Gary R., Gavaghan, David J.
Computational methods and associated software implementations are central to every field of scientific investigation. Modern biological research, particularly within systems biology, has relied heavily on the development of software tools to process
Externí odkaz:
http://arxiv.org/abs/2402.04722
Autor:
Herriott, Lara, Capel, Henriette L., Ellmen, Isaac, Schofield, Nathan, Zhu, Jiayuan, Lambert, Ben, Gavaghan, David, Bouros, Ioana, Creswell, Richard, Gallagher, Kit
Mathematical models play a crucial role in understanding the spread of infectious disease outbreaks and influencing policy decisions. These models aid pandemic preparedness by predicting outcomes under hypothetical scenarios and identifying weaknesse
Externí odkaz:
http://arxiv.org/abs/2310.13468
Autor:
Creswell, Richard, Shepherd, Katherine M., Lambert, Ben, Mirams, Gary R., Lei, Chon Lok, Tavener, Simon, Robinson, Martin, Gavaghan, David J.
Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solvers which seem sufficiently accurate for the forward problem, i.e., fo
Externí odkaz:
http://arxiv.org/abs/2307.00749
Autor:
Gallagher, Kit, Bouros, Ioana, Fan, Nicholas, Hayman, Elizabeth, Heirene, Luke, Lamirande, Patricia, Lemenuel-Diot, Annabelle, Lambert, Ben, Gavaghan, David, Creswell, Richard
Publikováno v:
Journal of Open Research Software, 12(1), p. 3
Epiabm is a fully tested, open-source software package for epidemiological agent-based modelling, re-implementing the well-known CovidSim model from the MRC Centre for Global Infectious Disease Analysis at Imperial College London. It has been develop
Externí odkaz:
http://arxiv.org/abs/2212.04937
Autor:
Lambert, Ben, Lei, Chon Lok, Robinson, Martin, Clerx, Michael, Creswell, Richard, Ghosh, Sanmitra, Tavener, Simon, Gavaghan, David
Ordinary differential equation models are used to describe dynamic processes across biology. To perform likelihood-based parameter inference on these models, it is necessary to specify a statistical process representing the contribution of factors no
Externí odkaz:
http://arxiv.org/abs/2210.01592
When modelling time series, it is common to decompose observed variation into a "signal" process, the process of interest, and "noise", representing nuisance factors that obfuscate the signal. To separate signal from noise, assumptions must be made a
Externí odkaz:
http://arxiv.org/abs/2011.04854
High dimensional integration is essential to many areas of science, ranging from particle physics to Bayesian inference. Approximating these integrals is hard, due in part to the difficulty of locating and sampling from regions of the integration dom
Externí odkaz:
http://arxiv.org/abs/2005.11300
Akademický článek
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Publikováno v:
In Computer Methods and Programs in Biomedicine October 2023 240
Autor:
Clerx, Michael, Robinson, Martin, Lambert, Ben, Lei, Chon Lok, Ghosh, Sanmitra, Mirams, Gary R., Gavaghan, David J.
Time series models are ubiquitous in science, arising in any situation where researchers seek to understand how a system's behaviour changes over time. A key problem in time series modelling is \emph{inference}; determining properties of the underlyi
Externí odkaz:
http://arxiv.org/abs/1812.07388