7 citations · 10 across the 5 of their papers we have counts for
5 papers
Bayesian estimation of optical constants using mixtures of Gaussian process experts
Teemu Härkönen, Hui Chen, Erik Vartiainen
We propose modeling absorption spectrum measurements as mixtures of Gaussian process experts. This enables us to construct a flexible statistical model for interpolating and extrap…
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…
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…
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…