102 citations · 277 across the 26 of their papers we have counts for
26 papers
Flexible Transformations for Bayesian Score Calibration
Adam Bretherton, Joshua J. Bon, David J. Warne +2
Modern statistical models are growing increasingly complex in an effort to realistically capture system dynamics. Using standard simulation-based inference, these models may be com…
Bayesian inference for ordinary differential equations models with heteroscedastic measurement error
Selva Salimi, David J. Warne, Christopher Drovandi
Ordinary differential equation (ODE) models are widely used to describe systems in many areas of science. To ensure these models provide accurate and interpretable representations…
Preconditioned Robust Neural Posterior Estimation for Misspecified Simulators
Ryan P. Kelly, David T. Frazier, David J. Warne +1
Simulation-based inference (SBI) enables parameter estimation for complex stochastic models with intractable likelihoods when model simulation is feasible. Neural posterior estimat…
Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
Thomas P. Steele, David J. Warne
Stochastic models of reaction networks are widely used to capture intrinsic noise in complex systems in the life sciences. Typical formulations of these models are based on Markov…
Simulation-based Bayesian inference under model misspecification
Ryan P. Kelly, David J. Warne, David T. Frazier +3
Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulatio…
A Principled Approach to Bayesian Transfer Learning
Adam Bretherton, Joshua J. Bon, David J. Warne +2
Updating information given some observed data is the core tenet of Bayesian inference. Bayesian transfer learning extends this idea by incorporating information…
The Statistical Accuracy of Neural Posterior and Likelihood Estimation
David T. Frazier, Ryan Kelly, Christopher Drovandi +1
Neural posterior estimation (NPE) and neural likelihood estimation (NLE) are machine learning approaches that provide accurate posterior, and likelihood, approximations in complex…
A Comprehensive Guide to Simulation-based Inference in Computational Biology
Xiaoyu Wang, Ryan P. Kelly, Adrianne L. Jenner +2
Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially w…
Bayesian design for mathematical models of fruit growth based on misspecified prior information
Nushrath Najimuddin, David J. Warne, Helen Thompson +1
Bayesian design can be used for efficient data collection over time when the process can be described by the solution to an ordinary differential equation (ODE). Typically, Bayesia…
Mathematical modelling and uncertainty quantification for analysis of biphasic coral reef recovery patterns
David J. Warne, Kerryn Crossman, Grace E. M. Heron +6
Coral reefs are increasingly subjected to major disturbances threatening the health of marine ecosystems. Substantial research underway to develop intervention strategies that assi…
Likelihood-based inference, identifiability and prediction using count data from lattice-based random walk models
Yihan Liu, David J Warne, Matthew J Simpson
In vitro cell biology experiments are routinely used to characterize cell migration properties under various experimental conditions. These experiments can be interpreted using lat…
Preconditioned Neural Posterior Estimation for Likelihood-free Inference
Xiaoyu Wang, Ryan P. Kelly, David J. Warne +1
Simulation based inference (SBI) methods enable the estimation of posterior distributions when the likelihood function is intractable, but where model simulation is feasible. Popul…
Beyond expected values: Making environmental decisions using value of information analysis when measurement outcome matters
Morenikeji D. Akinlotan, David J. Warne, Kate J. Helmstedt +5
In ecological and environmental contexts, management actions must sometimes be chosen urgently. Value of information (VoI) analysis provides a quantitative toolkit for projecting t…
Communicating uncertainty in Indigenous sea Country monitoring with Bayesian statistics: towards more informed decision-making
Katherine Cure, Diego R Barneche, Martial Depczynski +14
First Nations Australians have a cultural obligation to look after land and sea Country, and Indigenous-partnered science is beginning to drive socially inclusive initiatives in co…
Generalised likelihood profiles for models with intractable likelihoods
David J. Warne, Oliver J. Maclaren, Elliot J. Carr +2
Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, th…
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Ryan P. Kelly, David J. Nott, David T. Frazier +2
Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical app…
Robust simulation design for generalized linear models in conditions of heteroscedasticity or correlation
Andrew Gill, David J. Warne, Antony M. Overstall +2
A meta-model of the input-output data of a computationally expensive simulation is often employed for prediction, optimization, or sensitivity analysis purposes. Fitting is enabled…
Bayesian score calibration for approximate models
Joshua J Bon, David J Warne, David J Nott +1
Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging si…
Efficient Multifidelity Likelihood-Free Bayesian Inference with Adaptive Computational Resource Allocation
Thomas P Prescott, David J Warne, Ruth E Baker
Likelihood-free Bayesian inference algorithms are popular methods for calibrating the parameters of complex, stochastic models, required when the likelihood of the observed data is…
Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
David J. Warne, Thomas P. Prescott, Ruth E. Baker +1
Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistica…
A practical guide to pseudo-marginal methods for computational inference in systems biology
David J. Warne, Ruth E. Baker, Matthew J. Simpson
For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical…
Rapid Bayesian inference for expensive stochastic models
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardwa…
Vector operations for accelerating expensive Bayesian computations -- a tutorial guide
David J. Warne, Scott A. Sisson, Christopher Drovandi
Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parall…
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterising stochastic effects in biochemical system…