Results 51 to 60 of about 741,734 (189)

Hierarchical linear models in education sciences: an application [PDF]

, 2009
The importance of hierarchical structured data analysis, based on appropriate statistical models, is very well known in several research areas.
Oliveira, Teresa, Valente, Vítor
core  

Chiral non-linear sigma-models as models for topological superconductivity

, 2000
We study the mechanism of topological superconductivity in a hierarchical chain of chiral non-linear sigma-models (models of current algebra) in one, two, and three spatial dimensions. The models have roots in the 1D Peierls-Frohlich model and illustrate
A. Fetter, A. G. Abanov, A. G. Abanov, A. G. Abanov, A. G. Abanov, A. J. Heeger, A. M. J. Schakel, A. M. Polyakov, D. Loss, D. Senechal, G. Fröhlich, I. Affleck, J. Goldstone, L. Pontrjagin, N. Dorey, P. A. Lee, P. B. Wiegmann, P. B. Wiegmann, P. B. Wiegmann, P. Wiegmann, R. B. Laughlin, S. B. Treiman, T. Jaroszewicz, T. Jaroszewicz, X.-G. Wen, Y-H. Chen   +25 more
core   +1 more source

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models [PDF]

, 2012
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially ...
Ko, Young Jun, Seeger, Matthias
core   +2 more sources

Gravitational Clustering from Chi^2 Initial Conditions

, 2001
We consider gravitational clustering from primoridal non-Gaussian fluctuations provided by a $\chi^2$ model, as motivated by some models of inflation.
Bouchet F. R., Buchert T., Chodorowski M. J., Coles P., Fisher K., Hivon E., Kaiser N., Moscardini L., Roman Scoccimarro, Stirling A. J., Taylor A. N., Weinberg D.   +11 more
core   +1 more source

A Common Platform for Graphical Models in R: The gRbase Package

Journal of Statistical Software, 2005
The gRbase package is intended to set the framework for computer packages for data analysis using graphical models. The gRbase package is developed for the open source language, R, and is available for several platforms.
Claus Dethlefsen, Søren Højsgaard
doaj  

Generating 3D faces using Convolutional Mesh Autoencoders

, 2018
Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation.
Black, Michael J., Bolkart, Timo, Ranjan, Anurag, Sanyal, Soubhik   +3 more
core   +1 more source

Hierarchical Generalized Linear Models

Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996
SUMMARY We consider hierarchical generalized linear models which allow extra error components in the linear predictors of generalized linear models. The distribution of these components is not restricted to be normal; this allows a broader class of models, which includes generalized linear mixed models.
Y. Lee, J. A. Nelder
openaire   +1 more source

One Standard for All: Uniform Scale for Comparing Individuals and Groups in Hierarchical Bayesian Evidence Accumulation Modeling

Journal of Cognition
In recent years, a growing body of research uses Evidence Accumulation Models (EAMs) to study individual differences and group effects. This endeavor is challenging because fitting EAMs requires constraining one of the EAM parameters to be equal for all ...
Rotem Berkovich, Nachshon Meiran
doaj   +1 more source

Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model

Malaria Journal, 2019
Background Emerging resistance to anti-malarial drugs has led malaria researchers to investigate what covariates (parasite and host factors) are associated with resistance.
Saeed Sharifi-Malvajerdi, Feiyu Zhu, Colin B. Fogarty, Michael P. Fay, Rick M. Fairhurst, Jennifer A. Flegg, Kasia Stepniewska, Dylan S. Small   +7 more
doaj   +1 more source

Model-based clustering via linear cluster-weighted models

, 2015
A novel family of twelve mixture models with random covariates, nested in the linear $t$ cluster-weighted model (CWM), is introduced for model-based clustering.
Aitken, Andrews, Andrews, Antonio Punzo, Baek, Biernacki, Brent, Böhning, Campbell, Cellini, Chatzis, Cleveland, Dempster, Everitt, Flury, Fraley, Frühwirth-Schnatter, Gershenfeld, Greselin, Hennig, Hubert, Ingrassia, Lange, Leisch, McLachlan, McLachlan, McNicholas, McNicholas, McNicholas, McNicholas, Peel, Salvatore Ingrassia, Schwarz, Shoham, Simona C. Minotti, Titterington, Wand, Wedel, Zellner   +38 more
core   +1 more source