Computational challenges and temporal dependence in Bayesian nonparametric models

被引：0
|
作者：
Argiento, Raffaele [1 ,2 ]
Ruggiero, Matteo [1 ,2 ]
机构：
[1] Univ Torino, Turin, Italy
[2] Coll Carlo Alberto, Turin, Italy
来源：
STATISTICAL METHODS AND APPLICATIONS | 2018年 / 27卷 / 02期
关键词：
Bayesian dependent model; Conjugacy; Computation; Dirichlet; Transition function; FLEMING-VIOT PROCESS; DENSITY-ESTIMATION; SAMPLING METHODS; DIRICHLET; MIXTURE; INFERENCE; DISTRIBUTIONS;
D O I：
10.1007/s10260-017-0397-8
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Muller 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：8
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