TY - CONF
AU - Nagata, Kenji
AU - Watanabe, Sumio
ED - Köppen, Mario
ED - Kasabov, Nikola
ED - Coghill, George
PY - 2009
DA - 2009//
TI - Design of Exchange Monte Carlo Method for Bayesian Learning in Normal Mixture Models
BT - Advances in Neuro-Information Processing
SP - 696
EP - 706
PB - Springer Berlin Heidelberg
CY - Berlin, Heidelberg
AB - The exchange Monte Carlo (EMC) method was proposed as an improved algorithm of Markov chain Monte Carlo method, and its effectiveness has been shown in spin-glass simulation, Bayesian learning and many other applications. In this paper, we propose a new algorithm of EMC method with Gibbs sampler by using the hidden variable representing the component from which the datum is generated, and show its effectiveness by the simulation of Bayesian learning of normal mixture models.
SN - 978-3-642-02490-0
ID - 10.1007/978-3-642-02490-0_85
ER -