102 citations · 277 across the 26 of their papers we have counts for
9 papers · 1 filter
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-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…
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…
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…