Search results for "tilastolliset mallit"

Optimization of Linearized Belief Propagation for Distributed Detection

2020

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…

Statistical analysis of life sequence data

2016

On computation in statistical models with a psychophysical application

2004

FM Janne Kujalan tieteellisen laskennan väitöskirjan ”On computation in statistical models with a psychophysical application” (Laskennasta tilastollisissa malleissa psykofysiikkaan soveltaen) tarkastustilaisuus. Vastaväittäjänä FT Keijo Ruotsalainen (Oulun yliopisto) ja kustoksena professori Pekka Neittaanmäki.Kujala tehosti väitöskirjatutkimuksessaan tilastollisten ja fysikaalisten mallien laskentaa. Vaikka erinäisiin ongelmiin voidaan varsin helposti keksiä laskennallisia malleja, niiden käyttäminen ja vertailu on usein käytännössä mahdotonta tietokoneiden rajallisen tehon takia. Kujala etsi työssään vähemmän tehoa vaativia oikoteitä laskennallisten mallien käyttöön. Esimerkiksi kahden no…

Modelling phytoplankton in boreal lakes

2014

gllvm : Fast analysis of multivariate abundance data with generalized linear latent variable models in R

2019

1.There has been rapid development in tools for multivariate analysis based on fully specified statistical models or “joint models”. One approach attracting a lot of attention is generalized linear latent variable models (GLLVMs). However, software for fitting these models is typically slow and not practical for large datsets. 2.The R package gllvm offers relatively fast methods to fit GLLVMs via maximum likelihood, along with tools for model checking, visualization and inference. 3.The main advantage of the package over other implementations is speed e.g. being two orders of magnitude faster, and capable of handling thousands of response variables. These advances come from using variationa…

Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling : A Comment on Yuan and Deng (2021)

2023

In their article, Yuan and Deng argue that a structural parameter under partial least squares structural equation modeling (PLS-SEM) is zero if and only if the same structural parameter is zero under covariance-based structural equation modeling (CB-SEM). Yuan and Deng then conclude that statistical tests on individual structural parameters assessing the null hypothesis of no effect can achieve the same purpose in CB-SEM and PLS-SEM. Our response to their article highlights that the relationship they find between PLS-SEM and CB-SEM structural parameters is not universally valid, and that consequently, tests on individual parameters in CB-SEM and PLS-SEM generally do not fulfill the same pur…