PRODUCT PARTITION MODELS FOR CHANGE POINT PROBLEMS
PRODUCT PARTITION MODELS FOR CHANGE POINT PROBLEMS
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DOI：
10.1214/aos/1176348521
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发表时间：
1992-03-01
影响因子：
4.5
通讯作者：
HARTIGAN, JA
中科院分区：
文献类型：
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作者：
BARRY, D;HARTIGAN, JA
产品分区模型假设数据的随机分区的不同组成部分中的观测是独立的。如果随机分区的概率分布在进行观测之前是某一乘积形式，则在给定观测的情况下它也是乘积形式。因此，乘积模型提供了一种方便的机制，用于允许数据对可能保持的分区进行加权；然后，可以通过首先对分区进行条件调整，然后对所有分区进行平均来进行关于特定未来观察的推断。这些模型以特别的计算简单性应用于变点问题，其中分区将观测序列划分为不同区域内的分量。我们表明，在适当选择先前的乘积模型后，观测结果最终可以确定近似的真实划分。
Product partition models assume that observations in different components of a random partition of the data are independent. If the probability distribution of random partitions is in a certain product form prior to making the observations, it is also in product form given the observations. The product model thus provides a convenient machinery for allowing the data to weight the partitions likely to hold; and inference about particular future observations may then be made by first conditioning on the partition and then averaging over all partitions. These models apply with special computational simplicity to change point problems, where the partitions divide the sequence of observations into components within which different regimes hold. We show, with appropriate selection of prior product models, that the observations can eventually determine approximately the true partition.