TY - JOUR
T1 - A blocked Gibbs sampler for NGG-mixture models via a priori truncation
AU - Argiento, Raffaele
AU - Bianchini, Ilaria
AU - Guglielmi, Alessandra
PY - 2016
Y1 - 2016
N2 - Abstract We define a new class of random probability
measures, approximating the well-known normalized generalized gamma (NGG) process. Our new process is defined
from the representation of NGG processes as discrete measures where the weights are obtained by normalization of
the jumps of Poisson processes and the support consists of
independent identically distributed location points, however
considering only jumps larger than a threshold ε. Therefore, the number of jumps of the new process, called ε-NGG
process, is a.s. finite. A prior distribution for ε can be elicited.
We assume such a process as the mixing measure in a mixture model for density and cluster estimation, and build an
efficient Gibbs sampler scheme to simulate from the posterior. Finally, we discuss applications and performance of
the model to two popular datasets, as well as comparison
with competitor algorithms, the slice sampler and a posteriori truncation.
AB - Abstract We define a new class of random probability
measures, approximating the well-known normalized generalized gamma (NGG) process. Our new process is defined
from the representation of NGG processes as discrete measures where the weights are obtained by normalization of
the jumps of Poisson processes and the support consists of
independent identically distributed location points, however
considering only jumps larger than a threshold ε. Therefore, the number of jumps of the new process, called ε-NGG
process, is a.s. finite. A prior distribution for ε can be elicited.
We assume such a process as the mixing measure in a mixture model for density and cluster estimation, and build an
efficient Gibbs sampler scheme to simulate from the posterior. Finally, we discuss applications and performance of
the model to two popular datasets, as well as comparison
with competitor algorithms, the slice sampler and a posteriori truncation.
KW - Keywords Bayesian nonparametric mixture models, Normalized generalized gamma process, Blocked Gibbs sampler, Finite dimensional approximation, A priori truncation method
KW - Keywords Bayesian nonparametric mixture models, Normalized generalized gamma process, Blocked Gibbs sampler, Finite dimensional approximation, A priori truncation method
UR - http://hdl.handle.net/10807/146938
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922750134&doi=10.1007/s11222-015-9549-6&partnerid=40&md5=7ca579e7b2d245b4beb91e448675ee67
U2 - 10.1007/s11222-015-9549-6
DO - 10.1007/s11222-015-9549-6
M3 - Article
SN - 0960-3174
VL - 26
SP - 641
EP - 661
JO - Statistics and Computing
JF - Statistics and Computing
ER -