83 citations · 136 across the 33 of their papers we have counts for
39 papers
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…
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…
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…
Investigating the Electronic and Magnetic Properties of NaFeMnO Cathode Materials with X-ray Compton Scattering
Veenavee Nipunika Kothalawala, Kosuke Suzuki, Johannes Nokelainen +21
We discuss electronic and magnetic properties of NaFeMnO, a promising Na-ion battery cathode material. Using x-ray Compton scattering, SQUID magnetometry, a…
Inhomogeneous Priors for Bayesian Inverse Problems
Babak Maboudi Afkham, Tomas Soto, Mirza Karamehmedovic +1
Many inverse problems arising in engineering and applied sciences involve unknown quantities with pronounced spatial inhomogeneity, such as localized defects or spatially varying m…
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…
Computational design of personalized drugs via robust optimization under uncertainty
Rabia Altunay, Jarkko Suuronen, Eero Immonen +2
Effective disease treatment often requires precise control of the release of the active pharmaceutical ingredient (API). In this work, we present a computational inverse design app…
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…
Optimising Kernel-based Multivariate Statistical Process Control
Zina-Sabrina Duma, Victoria Jorry, Tuomas Sihvonen +2
Multivariate Statistical Process Control (MSPC) is a framework for monitoring and diagnosing complex processes by analysing the relationships between multiple process variables sim…
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…
Uncertainty quantification for electrical impedance tomography using quasi-Monte Carlo methods
Laura Bazahica, Vesa Kaarnioja, Lassi Roininen
The theoretical development of quasi-Monte Carlo (QMC) methods for uncertainty quantification of partial differential equations (PDEs) is typically centered around simplified model…
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…
Statistical Batch-Based Bearing Fault Detection
Victoria Jorry, Zina-Sabrina Duma, Tuomas Sihvonen +2
In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in conditi…
Bayesian inversion with Student's t priors based on Gaussian scale mixtures
Angelina Senchukova, Felipe Uribe, Lassi Roininen
Many inverse problems focus on recovering a quantity of interest that is a priori known to exhibit either discontinuous or smooth behavior. Within the Bayesian approach to inverse…
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…
Denture reinforcement via topology optimization
Rabia Altunay, Kalevi Vesterinen, Pasi Alander +3
We present a computational design method that optimizes the reinforcement of dentures and increases the stiffness of dentures. Our approach optimally places reinforcement in the de…
A Statistical Framework and Analysis for Perfect Radar Pulse Compression
Neil K. Chada, Petteri Piiroinen, Lassi Roininen
Perfect radar pulse compression coding is a potential emerging field which aims at providing rigorous analysis and fundamental limit radar experiments. It is based on finding non-t…
Bayesian inversion with α-stable priors
Jarkko Suuronen, Tomás Soto, Neil K. Chada +1
We propose to use Lévy α-stable distributions for constructing priors for Bayesian inverse problems. The construction is based on Markov fields with stable-distributed increments.…
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…
Geometry parameter estimation for sparse X-ray log imaging
Angelina Senchukova, Jarkko Suuronen, Jere Heikkinen +1
We consider geometry parameter estimation in industrial sawmill fan-beam X-ray tomography. In such industrial settings, scanners do not always allow identification of the location…
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Sebastian Springer, Aldo Glielmo, Angelina Senchukova +5
In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call…
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…
Cauchy Markov Random Field Priors for Bayesian Inversion
Neil K. Chada, Lassi Roininen, Jarkko Suuronen
The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian, high-dimensional, multimodal an…