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stat.AP2026
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…
stat.AP2026
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…
stat.AP2024
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…