5 papers
Efficient Amortized Bayesian Inference for Markov Random Fields via Gradient-Informed Grid Selection
Laura Bazahica, Alejandra Avalos-Pacheco, Matthew Moores +1
Bayesian inference for models with intractable likelihoods, such as Markov random fields, poses a fundamental computational challenge due to the tradeoff between inferential accura…
Identifiability and amortized inference limitations in Kuramoto models
Emma Hannula, Jana de Wiljes, Matthew T. Moores +2
Bayesian inference is a powerful tool for parameter estimation and uncertainty quantification in dynamical systems. However, for nonlinear oscillator networks such as Kuramoto mode…
Here Be Dragons: Bimodal posteriors arise from numerical integration error in longitudinal models
Tess O'Brien, Matthew T. Moores, David Warton +1
Longitudinal models with dynamics governed by differential equations may require numerical integration alongside parameter estimation. We have identified a situation where the nume…
Annealed Leap-Point Sampler for Multimodal Target Distributions
Nicholas G. Tawn, Matthew T. Moores, Hugo Queniat +1
In Bayesian statistics, exploring high-dimensional multimodal posterior distributions poses major challenges for existing MCMC approaches. This paper introduces the Annealed Leap-P…
Bayesian modelling and quantification of Raman spectroscopy
Matthew Moores, Kirsten Gracie, Jake Carson +3
Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurate…