Exact posterior distributions and model selection criteria for multiple change-point detection problems

G. Rigaill; E. Lebarbier; S. Robin

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Exact posterior distributions and model selection criteria for multiple change-point detection problems

机译：多个变化点检测问题的精确后验分布和模型选择标准

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摘要

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.

机译：在分割问题中，由于变更点的离散性，推断变更点位置和模型选择是两个困难的问题。在贝叶斯环境中，我们为变量的后验分布（例如变化点的数量或其位置）得出精确，显式和易处理的公式。我们还证明了可以精确计算几个经典贝叶斯模型选择标准。所有这些结果都是基于一种有效的策略来探索整个细分空间，该细分空间非常大。我们在模拟数据和比较基因组杂交谱上说明了我们的方法。

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《Statistics and computing》 |2012年第4期|p.917-929|共13页
作者
G. Rigaill; E. Lebarbier; S. Robin;
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作者单位

AgroParisTech, UMR 518,75005 Paris, France,INRA, UMR 518,75005 Paris, France,Departement de Transfert, Institut Curie, 75005 Paris, France,Bioinformatics and Statistics, NKI-AVL, 1066 CX Amsterdam,Netherlands;

AgroParisTech, UMR 518,75005 Paris, France,INRA, UMR 518,75005 Paris, France;

AgroParisTech, UMR 518,75005 Paris, France,INRA, UMR 518,75005 Paris, France;

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正文语种 eng
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关键词
bayesian model selection; change-point detection; BIC; DIC; ICL; posterior distribution of change-points; posterior distribution of segments;

机译：贝叶斯模型选择;变更点检测;BIC;DIC;ICL;变更点的后验分布;段的后部分布;

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1. Bayesian Model Selection Approach to Multiple Change-Points Detection with Non-Local Prior Distributions [J] . Jiang Fei, Yin Guosheng, Dominici Francesca ACM transactions on knowledge discovery from data . 2019,第5期

机译：非局部先验分布的多变化点检测的贝叶斯模型选择方法
2. Exact Bayesian inference for off-line change-point detection in tree-structured graphical models [J] . Schwaller L., Robin S. Statistics and computing . 2017,第5期

机译：树结构图形模型中离线变化点检测的精确贝叶斯推断
3. On the influence of the proposal distributions on a reversible jump MCMC algorithm applied to the detection of multiple change-points [J] . R. Rotondi Computational statistics & data analysis . 2002,第3期

机译：提案分配对可逆跳转MCMC算法的影响，该算法适用于多个变更点的检测
4. Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems [C] . G. Rigaill, E. Lebarbier, S. Robin International Conference on Computational Statistics . 2010

机译：在分段空间和多个变化点检测问题的分段空间和模型选择上的精确分布
5. A multiple cue threshold learning model of selection and detection: Balancing judgmental accuracy with threshold learning. [D] . Roggio, April M. 2011

机译：选择和检测的多线索阈值学习模型：在阈值学习中平衡判断准确性。
6. Bayesian Model Selection For Incomplete Data using the Posterior Predictive Distribution [O] . Michael J. Daniels, Arkendu S. Chatterjee, Chenguang Wang -1

机译：使用后预测分布的贝叶斯模型选择不完整的数据
7. Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems [O] . Rigaill, Guillem, Lebarbier, Émilie, Robin, Stephane 2010

机译：分割空间上的精确后验分布和用于多个变化点检测问题的模型选择

Exact posterior distributions and model selection criteria for multiple change-point detection problems

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