TY - JOUR
T1 - Variational inference for multiplicative intensity models
AU - Lau, John W.
AU - Cripps, Edward
AU - Hui, Wendy
PY - 2020/6/1
Y1 - 2020/6/1
N2 - We extend variational inference approximation of probability density functions to multiplicative intensity functions. For Bayesian nonparametrics, this provides a computationally efficient alternative to the blocked Gibbs sampler described in Ishwaran and James (2004). Simulation results are presented to demonstrate performance.
AB - We extend variational inference approximation of probability density functions to multiplicative intensity functions. For Bayesian nonparametrics, this provides a computationally efficient alternative to the blocked Gibbs sampler described in Ishwaran and James (2004). Simulation results are presented to demonstrate performance.
KW - Bayesian nonparametrics
KW - Multiplicative intensity
KW - Variational inference
UR - http://www.scopus.com/inward/record.url?scp=85079409837&partnerID=8YFLogxK
U2 - 10.1016/j.spl.2020.108720
DO - 10.1016/j.spl.2020.108720
M3 - Article
AN - SCOPUS:85079409837
SN - 0167-7152
VL - 161
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
M1 - 108720
ER -