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Rudoy, Daniel

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Rudoy, Daniel
Author's Publications: search.filters.isAuthorOfPublication.f445f26c-2619-4c28-9c52-c7f92cd5238d

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    Bayesian Changepoint Detection Through Switching Regressions: Contact Point Determination in Material Indentation Experiments
    (Institute of Electrical and Electronics Engineers, 2007) Yuen, Shelten G.; Rudoy, Daniel; Howe, Robert; Wolfe, Patrick J.
    Material indentation is a popular method for determining the mechanical properties of biomaterials. The basic premise of an indentation experiment is to physically displace the sample using an indenter that measures resistive force, in order to formulate a force-displacement curve. However, doing so requires estimating the initial contact event between the indenter and the sample-a statistical changepoint detection problem that has not been rigorously addressed in the biomaterials literature to date. Here we adopt a hierarchical Bayesian approach to contact point determination based on switching regressions, which generalizes an algorithm popular with practitioners and enables both hyperparameter estimation as well as uncertainty quantification. Results using several experimentally obtained silicone indentation data sets indicate that our approach outperforms existing techniques.
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    Bayesian change-point analysis for atomic force microscopy and soft material indentation
    (Wiley-Blackwell, 2010) Rudoy, Daniel; Yuen, Shelten G.; Howe, Robert; Wolfe, Patrick J.
    Material 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.