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Conference Paper: Bayesian model selection approach to boundary detection with non-local priors
Title | Bayesian model selection approach to boundary detection with non-local priors |
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Authors | |
Issue Date | 2018 |
Publisher | Neural Information Processing Systems Foundation, Inc. Proceedings' web site is located at https://papers.nips.cc/ |
Citation | Thirty-second Conference on Neural Information Processing Systems, Montréal, Canada, 3-8 December 2018. In Bengio, S ... et al (eds.), Advances in Neural Information Processing Systems 31 (NIPS 2018 Proceedings) How to Cite? |
Abstract | Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. We speed up the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. Extensive simulation studies are conducted to compare the BMS with existing methods, and our approach is illustrated with application to the magnetic resonance imaging guided radiation therapy data. |
Description | Poster Session A |
Persistent Identifier | http://hdl.handle.net/10722/263695 |
DC Field | Value | Language |
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dc.contributor.author | Jiang, F | - |
dc.contributor.author | Yin, G | - |
dc.contributor.author | Dominici, F | - |
dc.date.accessioned | 2018-10-22T07:43:05Z | - |
dc.date.available | 2018-10-22T07:43:05Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Thirty-second Conference on Neural Information Processing Systems, Montréal, Canada, 3-8 December 2018. In Bengio, S ... et al (eds.), Advances in Neural Information Processing Systems 31 (NIPS 2018 Proceedings) | - |
dc.identifier.uri | http://hdl.handle.net/10722/263695 | - |
dc.description | Poster Session A | - |
dc.description.abstract | Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. We speed up the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. Extensive simulation studies are conducted to compare the BMS with existing methods, and our approach is illustrated with application to the magnetic resonance imaging guided radiation therapy data. | - |
dc.language | eng | - |
dc.publisher | Neural Information Processing Systems Foundation, Inc. Proceedings' web site is located at https://papers.nips.cc/ | - |
dc.relation.ispartof | Thirty-second Conference on Neural Information Processing Systems (NIPS 2018) | - |
dc.title | Bayesian model selection approach to boundary detection with non-local priors | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Jiang, F: feijiang@hku.hk | - |
dc.identifier.email | Yin, G: gyin@hku.hk | - |
dc.identifier.authority | Jiang, F=rp02185 | - |
dc.identifier.authority | Yin, G=rp00831 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.hkuros | 294016 | - |
dc.publisher.place | Canada | - |