375 search results - page 2 / 75 » Bayesian Inference for Sparse Generalized Linear Models |
NIPS
2007
13 years 6 months ago
2007
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
TSP
2011
12 years 11 months ago
2011
—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switc...
JMLR
2011
12 years 11 months ago
2011
We propose a fully Bayesian methodology for generalized kernel mixed models (GKMMs), which are extensions of generalized linear mixed models in the feature space induced by a repr...
PKDD
2010
Springer
13 years 3 months ago
2010
Springer
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...
ICASSP
2010
IEEE
13 years 5 months ago
2010
IEEE
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...