activity
20162020
collaborators

11 papers

eess.SP2020

Blind hierarchical deconvolution

Arttu Arjas, Lassi Roininen, Mikko J. Sillanpää +1

Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of mod…

math.ST2020

Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion

Muhammad Emzir, Sari Lasanen, Zenith Purisha +2

In this article, we study Bayesian inverse problems with multi-layered Gaussian priors. We first describe the conditionally Gaussian layers in terms of a system of stochastic parti…

stat.AP2020

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…

cs.CE2020

Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods

Jarkko Suuronen, Muhammad Emzir, Sari Lasanen +2

X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, chemical engineering, and geotechnical engineering. In this article,…

q-fin.PR2019

Brexit Risk Implied by the SABR Martingale Defect in the EUR-GBP Smile

Petteri Piiroinen, Lassi Roininen, Martin Simon

We construct a data-driven statistical indicator for quantifying the tail risk perceived by the EURGBP option market surrounding Brexit-related events. We show that under lognormal…

physics.geo-ph2019

A Bayesian-based approach to improving acoustic Born waveform inversion of seismic data for viscoelastic media

Kenneth Muhumuza, Lassi Roininen, Janne M. J. Huttunen +1

In seismic waveform inversion, the reconstruction of the subsurface properties is usually carried out using approximative wave propagation models to ensure computational efficiency…