2 citations · 2 across the 3 of their papers we have counts for
6 papers
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…
Robustifying Approximate Bayesian Computation
Chaya Weerasinghe, David T. Frazier, Ruben Loaiza-Maya +1
Approximate Bayesian computation (ABC) is one of the most popular "likelihood-free" methods. These methods have been applied in a wide range of fields by providing solutions to int…
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…