TY - GEN
T1 - Dirichlet process mixture models with multiple modalities
AU - Paisley, John
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2009/9/23
Y1 - 2009/9/23
N2 - The Dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. The set of parameters over which it is defined is generally used for a single, parametric distribution. We extend this idea to parameter spaces that characterize multiple distributions, or modalities. In this framework, observations containing multiple, incompatible pieces of information can be mixed upon, allowing for all information to inform the final clustering result. We provide a general MCMC sampling scheme and demonstrate this framework on a Gaussian-HMM mixture model applied to synthetic and Major League Baseball data. ©2009 IEEE.
AB - The Dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. The set of parameters over which it is defined is generally used for a single, parametric distribution. We extend this idea to parameter spaces that characterize multiple distributions, or modalities. In this framework, observations containing multiple, incompatible pieces of information can be mixed upon, allowing for all information to inform the final clustering result. We provide a general MCMC sampling scheme and demonstrate this framework on a Gaussian-HMM mixture model applied to synthetic and Major League Baseball data. ©2009 IEEE.
UR - http://ieeexplore.ieee.org/document/4959908/
UR - http://www.scopus.com/inward/record.url?scp=70349200757&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2009.4959908
DO - 10.1109/ICASSP.2009.4959908
M3 - Conference contribution
SN - 9781424423545
SP - 1613
EP - 1616
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -