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In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring

In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain.

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Date Created
  • 2019
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  • Text
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    Note
    • Partial requirement for: M.A., Arizona State University, 2019
      Note type
      thesis
    • Includes bibliographical references (pages 32-36)
      Note type
      bibliography
    • Field of study: Applied mathematics

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    by Ross Wallgren

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