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dc.contributor.authorRudoy, Daniel
dc.contributor.authorYuen, Shelten G.
dc.contributor.authorHowe, Robert D.
dc.contributor.authorWolfe, Patrick J.
dc.date.accessioned2017-07-13T21:12:39Z
dc.date.issued2010
dc.identifier.citationRudoy, Daniel, Shelten G. Yuen, Robert D. Howe, and Patrick J. Wolfe. 2010. “Bayesian Change-Point Analysis for Atomic Force Microscopy and Soft Material Indentation.” Journal of the Royal Statistical Society: Series C (Applied Statistics) (June 10): no–no. doi:10.1111/j.1467-9876.2010.00715.x.en_US
dc.identifier.issn0035-9254en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33439205
dc.description.abstractMaterial indentation studies, in which a probe is brought into controlled physical contact with an experimental sample, have long been a primary means by which scientists characterize the mechanical properties of materials. More recently, the advent of atomic force microscopy, which operates on the same fundamental principle, has in turn revolutionized the nanoscale analysis of soft biomaterials such as cells and tissues. This paper addresses the inferential problems associated with material indentation and atomic force microscopy, through a framework for the changepoint analysis of pre- and post-contact data that is applicable to experiments across a variety of physical scales. A hierarchical Bayesian model is proposed to account for experimentally observed changepoint smoothness constraints and measurement error variability, with efficient Monte Carlo methods developed and employed to realize inference via posterior sampling for parameters such as Young’s modulus, a key quantifier of material stiffness. These results are the first to provide the materials science community with rigorous inference procedures and uncertainty quantification, via optimized and fully automated high-throughput algorithms, implemented as the publicly available software package BayesCP. To demonstrate the consistent accuracy and wide applicability of this approach, results are shown for a variety of data sets from both macro- and micro-materials experiments—including silicone, neurons, and red blood cells—conducted by the authors and others.en_US
dc.description.sponsorshipEngineering and Applied Sciencesen_US
dc.language.isoen_USen_US
dc.publisherWiley-Blackwellen_US
dc.relation.isversionof10.1111/j.1467-9876.2010.00715.xen_US
dc.relation.hasversionhttp://arxiv.org/abs/0909.5438en_US
dash.licenseOAP
dc.subjectchangepoint detectionen_US
dc.subjectconstrained switching regressionsen_US
dc.subjecthierarchical Bayesian modelsen_US
dc.subjectindentation testingen_US
dc.subjectMarkov Chain Monte Carloen_US
dc.subjectYoung's modulusen_US
dc.titleBayesian change-point analysis for atomic force microscopy and soft material indentationen_US
dc.typeJournal Articleen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalJournal of the Royal Statistical Society: Series C (Applied Statistics)en_US
dash.depositing.authorHowe, Robert D.
dc.date.available2017-07-13T21:12:39Z
dc.identifier.doi10.1111/j.1467-9876.2010.00715.x*
dash.contributor.affiliatedRudoy, Daniel
dash.contributor.affiliatedWolfe, Patrick J.
dash.contributor.affiliatedHowe, Robert


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