83 citations · 136 across the 33 of their papers we have counts for
10 papers · 1 filter
An unsupervised kernel norm monitoring for fault detection in a time series photovoltaic system
Victoria Jorry, Zina-Sabrina Duma, Satu-Pia Reinikainen +2
Grid-connected photovoltaic systems (GCPVS) are generally robust but remain susceptible to faults that can compromise energy conversion efficiency or raise safety concerns. Promptl…
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…
Probabilistic multivariate statistical process control via kernel parameter uncertainty propagation
Zina-Sabrina Duma, Victoria Jorry, Ayesha Safraz +4
Kernel-based multivariate statistical process control (K-MSPC) extends classical monitoring to nonlinear industrial processes. Its performance depends critically on kernel paramete…
Spatiotemporal Dynamics of Conflict Occurrence and Fatalities in Ethiopia: A Bayesian Model and Predictive Insights Using Event-level Data (1997--2024)
Yassin Tesfaw Abebe, Abdu Mohammed Seid, Lassi Roininen +1
This study presents a spatiotemporal dual Bayesian model that examines both the occurrence and number of conflict fatalities using event-level data from Ethiopia (1997-2024), sourc…
Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability over Ethiopia Using Satellite Data and Multisource Covariates
Yassin Tesfaw Abebe, Abdu Mohammed Seid, Lassi Roininen +2
Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions wher…
Partially stochastic deep learning with uncertainty quantification for model predictive heating control
Emma Hannula, Arttu Häkkinen, Antti Solonen +3
Making the control of building heating systems more energy efficient is crucial for reducing global energy consumption and greenhouse gas emissions. Traditional rule-based control…
Log-Gaussian Cox Processes for Spatiotemporal Traffic Fatality Estimation in Addis Ababa
Yassin Tesfaw Abebe, Abdu Mohammed Seid, Lassi Roininen
We investigate the spatiotemporal dynamics of traffic accidents in Addis Ababa, Ethiopia, using 2016--2019 data. We formulate the traffic accident intensity as a log-Gaussian Cox P…
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…
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…