Computational challenges and temporal dependence in Bayesian nonparametric models

被引：0
|
作者：
Raffaele Argiento
Matteo Ruggiero
机构：
[1] University of Torino and Collegio Carlo Alberto,
来源：
Statistical Methods & Applications | 2018年 / 27卷
关键词：
Bayesian dependent model; Conjugacy; Computation; Dirichlet; Transition function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Müller et al. (Stat Methods Appl, 2017) provide an excellent review of several classes of Bayesian nonparametric models which have found widespread application in a variety of contexts, successfully highlighting their flexibility in comparison with parametric families. Particular attention in the paper is dedicated to modelling spatial dependence. Here we contribute by concisely discussing general computational challenges which arise with posterior inference with Bayesian nonparametric models and certain aspects of modelling temporal dependence.
引用
收藏
页码：231 / 238
页数：7
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