62 citations · 176 across the 12 of their papers we have counts for
6 papers · 1 filter
Independent vector analysis -- an introduction for statisticians
Miro Arvila, Klaus Nordhausen, Mika Sipilä +1
Blind source separation (BSS), particularly independent component analysis (ICA), has been widely used in various fields of science such as biomedical signal processing to recover…
Modelling multivariate spatio-temporal data with identifiable variational autoencoders
Mika Sipilä, Claudia Cappello, Sandra De Iaco +2
Modelling multivariate spatio-temporal data with complex dependency structures is a challenging task but can be simplified by assuming that the original variables are generated fro…
A Comparison of Joint Species Distribution Models for Percent Cover Data
Pekka Korhonen, Francis K. C. Hui, Jenni Niku +2
1. Joint species distribution models (JSDMs) have gained considerable traction among ecologists over the past decade, due to their capacity to answer a wide range of questions at b…
Nonlinear blind source separation exploiting spatial nonstationarity
Mika Sipilä, Klaus Nordhausen, Sara Taskinen
In spatial blind source separation the observed multivariate random fields are assumed to be mixtures of latent spatially dependent random fields. The objective is to recover laten…
Fast, universal estimation of latent variable models using extended variational approximations
Pekka Korhonen, Francis K. C. Hui, Jenni Niku +1
Generalized linear latent variable models (GLLVMs) are a class of methods for analyzing multi-response data which has garnered considerable popularity in recent years, for example,…
Stationary subspace analysis based on second-order statistics
Lea Flumian, Markus Matilainen, Klaus Nordhausen +1
In stationary subspace analysis (SSA) one assumes that the observable p-variate time series is a linear mixture of a k-variate nonstationary time series and a (p-k)-variate station…