7 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
Interpolated inverse discrete wavelet transforms in additive and non-additive spectral background correction
Teemu Härkönen, Erik Vartiainen
We demonstrate the applicability of using interpolated inverse discrete wavelet transforms as a general tool for modeling additive or multiplicative background or error signals in…
Mixtures of Gaussian Process Experts with SMC
Teemu Härkönen, Sara Wade, Kody Law +1
Gaussian processes are a key component of many flexible statistical and machine learning models. However, they exhibit cubic computational complexity and high memory constraints du…
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…