An efficient sampling algorithm with adaptations for Bayesian variable selection.

Araki, Takamitsu; Ikeda, Kazushi; Akaho, Shotaro

doi:10.1016/j.neunet.2014.09.010

Public

An efficient sampling algorithm with adaptations for Bayesian variable selection.

Published in Neural networks : the official journal of the International Neural Network Society • Jan 1, 2015

NobleIDark:/48914/w1/20260526/8DAB3CA3Record on NobleID

Authors:

Takamitsu Araki

,

Kazushi Ikeda

,

Shotaro Akaho

Abstract

In Bayesian variable selection, indicator model selection (IMS) is a class of well-known sampling algorithms, which has been used in various models. The IMS is a class of methods that uses pseudo-priors and it contains specific methods such as Gibbs variable selection (GVS) and Kuo and Mallick's (KM...

Discussions