Exact and efficient Bayesian inference for multiple changepoint problems
Exact and efficient Bayesian inference for multiple changepoint problems
复制标题
DOI：
10.1007/s11222-006-8450-8
复制
发表时间：
2006-06-01
影响因子：
2.2
通讯作者：
Fearnhead, P
中科院分区：
文献类型：
--
作者：
Fearnhead, P
我们演示了如何从一类变点数目未知的多变点模型的后验分布进行直接模拟。这类模型假定与连续变化点之间的数据段相关联的参数的后验分布之间是独立的。这种方法基于递归的使用，并且与产品分区模型的工作相关。该方法的计算复杂度是观测值个数的二次函数，但也有可能得到一个近似形式，它引入的误差可以忽略不计，其计算成本与观测值个数大致成线性关系。我们的方法可以是有用的，例如在MCMC算法中，即使当独立性假设不成立时也是如此。我们在煤矿灾害数据和测井数据上演示了我们的方法。我们的方法可以处理一系列模型，并且可以在几分钟内从后验分布进行精确模拟。
We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to work on product partition models. The computational complexity of the approach is quadratic in the number of observations, but an approximate version, which introduces negligible error, and whose computational cost is roughly linear in the number of observations, is also possible. Our approach can be useful, for example within an MCMC algorithm, even when the independence assumptions do not hold. We demonstrate our approach on coal-mining disaster data and on well-log data. Our method can cope with a range of models, and exact simulation from the posterior distribution is possible in a matter of minutes.