Bayesian Retrospective Multiple‐Changepoint Identification
Bayesian Retrospective Multiple‐Changepoint Identification
复制标题
贝叶斯回顾性多变化点识别
DOI：
--
复制
发表时间：
1994
期刊：
影响因子：
--
通讯作者：
D. Stephens
中科院分区：
文献类型：
--
作者：
D. Stephens
变化点识别在许多数据分析问题中很重要，例如工业控制和医疗诊断——给定一个数据序列，我们希望对序列中一个或多个点的位置做出推断，在这些点上模型或驱动系统的参数发生了变化。然而，对于长数据序列，分析（特别是在多个变点的情况下）可能变得难以计算，对于复杂的非线性模型，分析和传统的数值技术是不可行的。我们讨论了基于采样的技术，吉布斯采样器，在多变点问题中的使用，并演示了如何使用它来大大减少所涉及的计算负荷。此外，通常可以合理地假设数据模型本身相对于时间是连续的，即在更改点是连续的。这需要对变更点问题进行连续的参数表示，这也会导致计算困难。我们演示了如何使用吉布斯采样器在这类问题中很容易地做出推断。我们研究了三个例子：一个基于二项式数据模型的简单离散双变点问题；连续切换线性回归问题；一个连续的、非线性的、多变点问题。
Changepoint identification is important in many data analysis problems, such as industrial control and medical diagnosis–given a data sequence, we wish to make inference about the location of one or more points of the sequence at which there is a change in the model or parameters driving the system. For long data sequences, however, analysis (especially in the multiple‐changepoint case) can become computationally prohibitive, and for complex non‐linear models analytical and conventional numerical techniques are infeasible. We discuss the use of a sampling‐based technique, the Gibbs sampler, in multiple‐changepoint problems and demonstrate how it can be used to reduce the computational load involved considerably. Also, often it is reasonable to presume that the data model itself is continuous with respect to time, i.e. continuous at the changepoints. This necessitates a continuous parameter representation of the changepoint problem, which also leads to computational difficulties. We demonstrate how inferences can be made readily in such problems by using the Gibbs sampler. We study three examples: A simple discrete two‐changepoint problem based on a binomial data model; a continuous switching linear regression problem; a continuous, non‐linear, multiple‐changepoint problem.