TY - GEN
T1 - Annealed SMC samplers for Dirichlet process mixture models
AU - Ulker, Yener
AU - Gunsel, Bilge
AU - Cemgil, Ali Taylan
PY - 2010
Y1 - 2010
N2 - In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an effective annealing procedure that prevents the algorithm to get trapped in a local mode. We evaluate the performance in a Bayesian density estimation problem with unknown number of components. The simulation results suggest that the proposed algorithm represents the target posterior much more accurately and provides significantly smaller Monte Carlo error when compared to particle filtering.
AB - In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an effective annealing procedure that prevents the algorithm to get trapped in a local mode. We evaluate the performance in a Bayesian density estimation problem with unknown number of components. The simulation results suggest that the proposed algorithm represents the target posterior much more accurately and provides significantly smaller Monte Carlo error when compared to particle filtering.
KW - Bayesian nonparametrics
KW - Dirichlet process mixture
KW - Sequential monte carlo
UR - http://www.scopus.com/inward/record.url?scp=78149484264&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.688
DO - 10.1109/ICPR.2010.688
M3 - Conference contribution
AN - SCOPUS:78149484264
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2808
EP - 2811
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -