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Three Statistical Technologies with High Potential in Biological Imaging and Modeling

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Book cover Computational Approaches in Molecular Radiation Biology

Part of the book series: Basic Life Sciences ((BLSC,volume 63))

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Abstract

The three technologies that are surveyed here are (1) wavelet approximations, (2) hidden Markov models, and (3) the Markov chain Renaissance. The intention of the article is to provide an introduction to the benefits these technologies offer and to explain as far as possible the sources of their effectiveness. We also hope to suggest some useful relationships between these technologies and issues of importance on the agenda of biological and medical research.

Research partially supported by NSF DMS92-11634

Research partially supported by ARO Grant DAAL03-91-G-0110 and NSF DMS92-11634

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Pennsylvania, Philadelphia, PA, 19104-6302, USA

    Moshe Fridman & J. Michael Steele

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  1. Moshe Fridman
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  2. J. Michael Steele
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Editors and Affiliations

  1. U.S. Department of Energy, Gaithersburg, Maryland, USA

    Matesh N. Varma

  2. Lawrence Berkeley Laboratory, Berkeley, California, USA

    Aloke Chatterjee

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Fridman, M., Steele, J.M. (1994). Three Statistical Technologies with High Potential in Biological Imaging and Modeling. In: Varma, M.N., Chatterjee, A. (eds) Computational Approaches in Molecular Radiation Biology. Basic Life Sciences, vol 63. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9788-6_15

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  • DOI: https://doi.org/10.1007/978-1-4757-9788-6_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4757-9790-9

  • Online ISBN: 978-1-4757-9788-6

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