Sequential Bayesian Analysis of Multivariate Count Data
Sequential Bayesian Analysis of Multivariate Count Data
复制标题
多元计数数据的序贯贝叶斯分析
DOI：
10.1214/17-ba1054
复制
发表时间：
2016
影响因子：
4.4
通讯作者：
R. Soyer
中科院分区：
文献类型：
--
作者：
Tevfik Aktekin;Nicholas G. Polson;R. Soyer
我们开发了一类新的动态多变量泊松计数模型，允许快速在线更新，我们将这些模型称为多变量泊松尺度β（MPSB）。MPSB模型允许计数中的序列依赖性以及在随机公共环境下跨多个序列的依赖性。其他显着的功能包括状态传播和预测似然密度的分析形式。通过更新静态模型参数的足够统计量来进行顺序更新，从而产生完全适应的粒子学习算法和一类新的预测似然和边缘分布，我们将其称为（动态）多变量融合超几何负二项分布（MCHG-NB）和动态多变量负二项分布（DMNB）。为了说明我们的方法，我们使用了各种模拟研究，并计算了每周非耐用品消费者需求的数据。
We develop a new class of dynamic multivariate Poisson count models that allow for fast online updating and we refer to these models as multivariate Poisson-scaled beta (MPSB). The MPSB model allows for serial dependence in the counts as well as dependence across multiple series with a random common environment. Other notable features include analytic forms for state propagation and predictive likelihood densities. Sequential updating occurs through the updating of the sufficient statistics for static model parameters, leading to a fully adapted particle learning algorithm and a new class of predictive likelihoods and marginal distributions which we refer to as the (dynamic) multivariate confluent hyper-geometric negative binomial distribution (MCHG-NB) and the the dynamic multivariate negative binomial (DMNB) distribution. To illustrate our methodology, we use various simulation studies and count data on weekly non-durable goods consumer demand.
多元非齐次时空泊松过程的非参数贝叶斯分割。
影响因子：
4.4
作者：
Ding,Mingtao;He,Lihan;Dunson,David;Carin,Lawrence
通讯作者：
Carin,Lawrence
影响因子：
2.4
作者：
Gramacy, Robert B.;Polson, Nicholas G.
通讯作者：
Polson, Nicholas G.