Assessing brain activity through spatial bayesian variable selection
نویسندگان
چکیده
منابع مشابه
Assessing brain activity through spatial Bayesian variable selection.
Statistical parametric mapping (SPM), relying on the general linear model and classical hypothesis testing, is a benchmark tool for assessing human brain activity using data from fMRI experiments. Friston et al. discuss some limitations of this frequentist approach and point out promising Bayesian perspectives. In particular, a Bayesian formulation allows explicit modeling and estimation of act...
متن کاملfMRI group analysis with Spatial Bayesian Variable Selection
Introduction: In recent times, Bayesian approaches have been increasingly popular in fMRI data analysis. One obvious appeal of the Bayesian approach is its interpretability. Instead of performing a hypothesis based inference at each voxel with an artificial threshold for declaration of activation, in the Bayesian approach we simply estimate the posterior probability of a voxel being active base...
متن کاملBayesian recursive variable selection
In this work we introduce a new model space prior for Bayesian variable selection in linear regression. This prior is designed based on a recursive constructive procedure that randomly generates models by including variables in a stagewise fashion. We provide a recipe for carrying out Bayesian variable selection and model averaging using this prior, and show that it possesses several desirable ...
متن کاملBayesian Shrinkage Variable Selection
We introduce a new Bayesian approach to the variable selection problem which we term Bayesian Shrinkage Variable Selection (BSVS ). This approach is inspired by the Relevance Vector Machine (RVM ), which uses a Bayesian hierarchical linear setup to do variable selection and model estimation. RVM is typically applied in the context of kernel regression although it is also suitable in the standar...
متن کاملBayesian Grouped Variable Selection
Traditionally, variable selection in the context of linear regression has been approached using optimization based approaches like the classical Lasso. Such methods provide a sparse point estimate with respect to regression coefficients but are unable to provide more information regarding the distribution of regression coefficients like expectation, variance estimates etc. In the recent years, ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: NeuroImage
سال: 2003
ISSN: 1053-8119
DOI: 10.1016/s1053-8119(03)00360-4