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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- beta-bernoulli process
- gaussian process
- Markov Chain Monte Carlo
- Fluorescence microscopy--Mathematical models.
- Fluorescence Microscopy
- Bayesian statistical decision theory--Scientific applications.
- Bayesian statistical decision theory
- Partial requirement for: M.A., Arizona State University, 2019Note typethesis
- Includes bibliographical references (pages 32-36)Note typebibliography
- Field of study: Applied mathematics