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David J Warne

Showing results (1-10 of 19) with videos related to

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Journal of Theoretical Biology|December 29, 2025
Parameter-wise predictions and sensitivity analysis for random walk models in the life sciencesYihan Liu, David J Warne, Matthew J Simpson
Physical Review. E|November 20, 2024
Likelihood-based inference, identifiability, and prediction using count data from lattice-based random walk modelsYihan Liu, David J Warne, Matthew J Simpson
Journal of Computational Physics|August 18, 2025
Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocationThomas P Prescott, David J Warne, Ruth E Baker
Bulletin of Mathematical Biology|March 1, 2019
Using Experimental Data and Information Criteria to Guide Model Selection for Reaction-Diffusion Problems in Mathematical BiologyDavid J Warne, Ruth E Baker, Matthew J Simpson
International Journal of Data Science and Analytics|May 9, 2022
Explainability of the COVID-19 epidemiological model with nonnegative tensor factorizationThirunavukarasu Balasubramaniam, David J Warne, Richi Nayak, et al.
Journal of the Royal Society, Interface|April 9, 2019
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-artDavid J Warne, Ruth E Baker, Matthew J Simpson
Biophysical Journal|October 17, 2017
Optimal Quantification of Contact Inhibition in Cell PopulationsDavid J Warne, Ruth E Baker, Matthew J Simpson
Journal of Theoretical Biology|April 1, 2020
A practical guide to pseudo-marginal methods for computational inference in systems biologyDavid J Warne, Ruth E Baker, Matthew J Simpson
China CDC Weekly|September 4, 2023
Comparative Analysis of Vaccine Inequity and COVID-19 Transmission Amid the Omicron Variant Among Countries - Six Countries, Asia-Pacific Region, 2022Jingli Yang, Hannah McClymont, David J Warne, et al.
Journal of Mathematical Biology|February 15, 2024
Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computationXiaoyu Wang, Adrianne L Jenner, Robert Salomone, et al.
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
Journal of Theoretical Biology|December 29, 2025
Parameter-wise predictions and sensitivity analysis for random walk models in the life sciencesYihan Liu, David J Warne, Matthew J Simpson
Physical Review. E|November 20, 2024
Likelihood-based inference, identifiability, and prediction using count data from lattice-based random walk modelsYihan Liu, David J Warne, Matthew J Simpson
Journal of Computational Physics|August 18, 2025
Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocationThomas P Prescott, David J Warne, Ruth E Baker
Bulletin of Mathematical Biology|March 1, 2019
Using Experimental Data and Information Criteria to Guide Model Selection for Reaction-Diffusion Problems in Mathematical BiologyDavid J Warne, Ruth E Baker, Matthew J Simpson
International Journal of Data Science and Analytics|May 9, 2022
Explainability of the COVID-19 epidemiological model with nonnegative tensor factorizationThirunavukarasu Balasubramaniam, David J Warne, Richi Nayak, et al.
Journal of the Royal Society, Interface|April 9, 2019
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-artDavid J Warne, Ruth E Baker, Matthew J Simpson
Biophysical Journal|October 17, 2017
Optimal Quantification of Contact Inhibition in Cell PopulationsDavid J Warne, Ruth E Baker, Matthew J Simpson
Journal of Theoretical Biology|April 1, 2020
A practical guide to pseudo-marginal methods for computational inference in systems biologyDavid J Warne, Ruth E Baker, Matthew J Simpson
China CDC Weekly|September 4, 2023
Comparative Analysis of Vaccine Inequity and COVID-19 Transmission Amid the Omicron Variant Among Countries - Six Countries, Asia-Pacific Region, 2022Jingli Yang, Hannah McClymont, David J Warne, et al.
Journal of Mathematical Biology|February 15, 2024
Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computationXiaoyu Wang, Adrianne L Jenner, Robert Salomone, et al.
Pageof 2