Nonparametric Bayesian models through probit stick-breaking processes.
Nonparametric Bayesian models through probit stick-breaking processes.
复制标题
DOI：
10.1214/11-ba605
复制
发表时间：
2011-03-01
影响因子：
4.4
通讯作者：
Dunson DB
中科院分区：
文献类型：
--
作者：
Rodríguez A;Dunson DB
我们描述了一类新的贝叶斯非参数先验的基础上坚持打破建设的过程中的权重被构造为正常的随机变量的概率变换。我们表明，这些先验是非常灵活的，使我们能够生成各种各样的模型，同时保持计算的简单性。特别强调的是丰富的时间和空间过程，这是适用于金融和生态两个问题的建设。
We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.