Title

Using Dirichlet Process Priors For Bayesian Mixture Clustering

Author

Xiao Huang, Washington University in St. LouisFollow

Author's School

Graduate School of Arts & Sciences

Author's Department/Program

Mathematics

Language

English (en)

Date of Award

5-24-2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Chair and Committee

Stanley Sawyer

Abstract

We describe a non-parametric Bayesian model using genotype data to classify individuals among populations where the total number of populations is unknown. The model assumes that a population is characterized by a set of allele frequencies that follow multinomial distributions. The Dirichlet Process is applied as the prior distribution. The method estimates the number of populations together with the allele frequencies and the ancestry coefficients of each individual. Distance matrices and bootstrap support numbers based on MCMC runs are generated to create a phylogeny of the ancestral populations.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7707ZHC

Recommended Citation

Huang, Xiao, "Using Dirichlet Process Priors For Bayesian Mixture Clustering" (2011). All Theses and Dissertations (ETDs). 864.
https://openscholarship.wustl.edu/etd/864