Description
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 diff
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Contributors
- Wallgren, Ross (Author)
- Presse, Steve (Thesis advisor)
- Armbruster, Hans (Thesis advisor)
- McCulloch, Robert (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2019
Subjects
- Mathematics
- Statistics
- Biophysics
- Bayesian
- beta-bernoulli process
- gaussian process
- Markov Chain Monte Carlo
- Microscopy
- superresolution
- Fluorescence microscopy--Mathematical models.
- Fluorescence Microscopy
- Bayesian statistical decision theory--Scientific applications.
- Bayesian statistical decision theory
Resource Type
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Note
- Partial requirement for: M.A., Arizona State University, 2019Note typethesis
- Includes bibliographical references (pages 32-36)Note typebibliography
- Field of study: Applied mathematics
Citation and reuse
Statement of Responsibility
by Ross Wallgren