38 citations · 86 across the 15 of their papers we have counts for
16 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…
Statistical Estimation of Mean Lorentzian Line Width in Spectra by Gaussian Processes
Erik Kuitunen, Matthew T. Moores, Teemu Härkönen
We propose a statistical approach for estimating the mean line width in spectra comprising Lorentzian, Gaussian, or Voigt line shapes. Our approach uses Gaussian processes in two s…
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies
Teemu Härkönen, Erik M. Vartiainen, Lasse Lensu +2
We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This…
A Log-Gaussian Cox Process with Sequential Monte Carlo for Line Narrowing in Spectroscopy
Teemu Härkönen, Emma Hannula, Matthew T. Moores +2
We propose a statistical model for narrowing line shapes in spectroscopy that are well approximated as linear combinations of Lorentzian or Voigt functions. We introduce a log-Gaus…
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…
Spatial Statistics
Noel Cressie, Matthew T. Moores
Spatial statistics is an area of study devoted to the statistical analysis of data that have a spatial label associated with them. Geographers often refer to the "location informat…
Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants
Quan Vu, Matthew T. Moores, Andrew Zammit-Mangion
Markov chain Monte Carlo methods for exponential family models with intractable normalizing constant, such as the exchange algorithm, require simulations of the sufficient statisti…
Bayesian quantification for coherent anti-Stokes Raman scattering spectroscopy
Teemu Härkönen, Lassi Roininen, Matthew T. Moores +1
We propose a Bayesian statistical model for analyzing coherent anti-Stokes Raman scattering (CARS) spectra. Our quantitative analysis includes statistical estimation of constituent…
Bayesian Computation with Intractable Likelihoods
Matthew T. Moores, Anthony N. Pettitt, Kerrie Mengersen
This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evalu…
A review on statistical inference methods for discrete Markov random fields
Julien Stoehr
Developing satisfactory methodology for the analysis of Markov random field is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant…
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…
Scalable Bayesian Inference for the Inverse Temperature of a Hidden Potts Model
Matthew T. Moores, Geoff K. Nicholls, Anthony N. Pettitt +1
The inverse temperature parameter of the Potts model governs the strength of spatial cohesion and therefore has a major influence over the resulting model fit. A difficulty arises…
An external field prior for the hidden Potts model, with application to cone-beam computed tomography
Matthew T. Moores, Catriona E. Hargrave, Fiona Harden +1
In images with low contrast-to-noise ratio (CNR), the information gain from the observed pixel values can be insufficient to distinguish foreground objects. A Bayesian approach to…
Pre-processing for approximate Bayesian computation in image analysis
Matthew T. Moores, Christopher C. Drovandi, Kerrie Mengersen +1
Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computa…