Bayesian Variable Selection in Clustering and Hierarchical Mixture Modeling
<p>Clustering methods are designed to separate heterogeneous data into groups of similar objects such that objects within a group are similar, and objects in different groups are dissimilar. From the machine learning perspective, clustering can also be viewed as one of the most important topic...
Main Author: | Lin, Lin |
---|---|
Other Authors: | West, Mike |
Published: |
2012
|
Subjects: | |
Online Access: | http://hdl.handle.net/10161/5846 |
Similar Items
-
Analysis of Otolith Microchemistry Using Bayesian Hierarchical Mixture Models
by: Pflugeisen, Bethann Mangel
Published: (2010) -
Bayesian Mixture Modeling Approaches for Intermediate Variables and Causal Inference
by: Schwartz, Scott Lee
Published: (2010) - Bayesian Modeling and Variable Selection for Complex Data
-
Generalized Gaussian process models with Bayesian variable selection
Published: (2011) -
Bayesian variable selection for GLM
by: Wang, Xinlei
Published: (2008)