18 citations · 33 across the 9 of their papers we have counts for
9 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…
Knots and variance ordering of sequential Monte Carlo algorithms
Joshua J Bon, Anthony Lee
Sequential Monte Carlo algorithms, or particle filters, are widely used for approximating intractable integrals, particularly those arising in Bayesian inference and state-space mo…
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…
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
Conor Hassan, Joshua J Bon, Elizaveta Semenova +2
Federated learning (FL) allows for collaborative model training across decentralized clients while preserving privacy by avoiding data sharing. However, current FL methods assume c…
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…
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…
Monte Carlo twisting for particle filters
Joshua J Bon, Christopher Drovandi, Anthony Lee
We consider the problem of designing efficient particle filters for twisted Feynman--Kac models. Particle filters using twisted models can deliver low error approximations of stati…
Accelerating sequential Monte Carlo with surrogate likelihoods
Joshua J Bon, Anthony Lee, Christopher Drovandi
Delayed-acceptance is a technique for reducing computational effort for Bayesian models with expensive likelihoods. Using a delayed-acceptance kernel for Markov chain Monte Carlo c…
Polling bias and undecided voter allocations: US Presidential elections, 2004 - 2016
Joshua J Bon, Timothy Ballard, Bernard Baffour
Accounting for undecided and uncertain voters is a challenging issue for predicting election results from public opinion polls. Undecided voters typify the uncertainty of swing vot…