Exact posterior distributions and model selection criteria for multiple change-point detection problems
Exact posterior distributions and model selection criteria for multiple change-point detection problems
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DOI：
10.1007/s11222-011-9258-8
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发表时间：
2012-07-01
影响因子：
2.2
通讯作者：
Robin, S.
中科院分区：
文献类型：
--
作者：
Rigaill, G.;Lebarbier, E.;Robin, S.
在分割问题中，由于变化点的离散性，变化点位置的推断和模型的选择是两个难点问题。在贝叶斯背景下，我们为变量的后验分布（如变点的数量或它们的位置）导出了精确、明确和易于处理的公式。我们还证明了几种经典贝叶斯模型选择准则可以精确地计算出来。所有这些结果都是基于一个有效的策略来探索整个分割空间，这是非常大的。我们在模拟数据和比较基因组杂交剖面上说明了我们的方法。
In segmentation problems, inference on change-point position and model selection are two difficult issues due to the discrete nature of change-points. In a Bayesian context, we derive exact, explicit and tractable formulae for the posterior distribution of variables such as the number of change-points or their positions. We also demonstrate that several classical Bayesian model selection criteria can be computed exactly. All these results are based on an efficient strategy to explore the whole segmentation space, which is very large. We illustrate our methodology on both simulated data and a comparative genomic hybridization profile.