Skip to main content

Bayesian Inference Frameworks for Fluorescence Microscopy Data Analysis

Abstract 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. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscop... (more)
Created Date 2019
Contributor Wallgren, Ross Tod (Author) / Presse, Steve (Advisor) / Armbruster, Hans (Advisor) / McCulloch, Robert (Committee member) / Arizona State University (Publisher)
Subject Mathematics / Statistics / Biophysics / bayesian / beta-bernoulli process / gaussian process / markov chain monte carlo / microscopy / superresolution
Type Masters Thesis
Extent 69 pages
Language English
Note Masters Thesis Applied Mathematics 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

  Full Text
2.3 MB application/pdf
Download Count: 10

Description Dissertation/Thesis