9 citations · 19 across the 16 of their papers we have counts for
16 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…
-PSD: Scalable Approximate SNR-Optimised Polynomial Stein Discrepancies
Minh-Long Nguyen, Thanh-Long Vu, Christopher Drovandi +2
Polynomial Stein discrepancies (PSD) provide a scalable alternative to kernel Stein methods for measuring sample quality and goodness-of-fit testing, but their statistical properti…
Hierarchical Bayes meets hierarchical forecasting: A flexible framework for level-focused forecasts
Arwen Nugteren, Mahdi Abolghasemi, Kerrie Mengersen +1
Decision-making in hierarchical systems requires probabilistic forecasts at all cross-sectional levels. Current hierarchical forecasting methods typically generate independent fore…
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…
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…
Exact Sampling of Gibbs Measures with Estimated Losses
David T. Frazier, Jeremias Knoblauch, Jack Jewson +1
A popular strategy for ameliorating some of the shortcomings of Bayesian posterior inference is to instead target a Gibbs measure based on losses that connect a parameter of intere…
Calibrated Generalized Bayesian Inference
David T. Frazier, Christopher Drovandi, Robert Kohn
We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors.…
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…
Bayesian Synthetic Likelihood
David T. Frazier, Christopher Drovandi, David J. Nott
Bayesian statistics is concerned with conducting posterior inference for the unknown quantities in a given statistical model. Conventional Bayesian inference requires the specifica…
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…
Automatically adapting the number of state particles in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbi…