{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some The code is fully compatible to both Matlab 7.x and GNU Octave 3.3.x. Abstract Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework.
[INTRODUCTION]: GLIM provides an excellent means for fitting gereralised linear models to data. I...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and S...
The glm-ie toolbox contains functionality for estimation and inference in generalised linear models ...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
Generalized linear models (GLMs) - such as logistic regression, Poisson regression, and robust regre...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
Generalized linear models unite a wide variety of statistical models in a common theoretical framewo...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
Generalized linear models unite a wide variety of statistical models in a common theoretical framewo...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear ...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear ...
Description A permutation test is used for inference in generalized linear models (glm). The procedu...
[INTRODUCTION]: GLIM provides an excellent means for fitting gereralised linear models to data. I...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...
{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and S...
The glm-ie toolbox contains functionality for estimation and inference in generalised linear models ...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins ...
Generalized linear models (GLMs) - such as logistic regression, Poisson regression, and robust regre...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
Generalized linear models unite a wide variety of statistical models in a common theoretical framewo...
Abstract. We present a framework for efficient, accurate approximate Bayesian inference in generaliz...
Generalized linear models unite a wide variety of statistical models in a common theoretical framewo...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear ...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear ...
Description A permutation test is used for inference in generalized linear models (glm). The procedu...
[INTRODUCTION]: GLIM provides an excellent means for fitting gereralised linear models to data. I...
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approxi...
The R package glmm enables likelihood-based inference for generalized linear mixed models with a can...