Single Molecule Data Analysis: An Introduction

Meysam Tavakoli1, J. Nicholas Taylor2, Chun-Biu Li2,6, Tamiki Komatsuzaki2 and Steve Pressé3,4,5

1Physics Department, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA

2Research Institute for Electronic Science, Hokkaido University, Kita 20 Nishi 10, Kita-Ku, Sapporo, 001-0020, Japan

3Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA

4Department of Cell and Integrative Physiology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA

5Department of Physics and School of Molecular Sciences, Arizona State University, Tempe, AZ, 85287, USA

6Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden

Contents

  1. I. Brief Introduction to Data Analysis
  2. II. Frequentist and Bayesian Parametric Approaches: A Brief Review
    1. A. Frequentist Inference
      1. 1. Maximum Likelihood Estimation: Applications to Hidden and Aggregated Markov Models
        1. Markov Models
        2. Hidden Markov Models
        3. Aggregated Markov Models
    2. B. Bayesian Inference
      1. 1. Priors
      2. 2. Uninformative Priors
      3. 3. Informative Priors
  3. III. Information Theory as a Data Analysis Tool
    1. A. Information Theory: Introduction to Key Quantities
    2. B. Information Theory in Model Inference
    3. C. Maximum Entropy and Bayesian Inference
    4. D. Applications of MaxEnt: Deconvolution Methods
      1. 1. MaxEnt Deconvolution: An Application to FCS
  4. IV. Model Selection
    1. A. Brief Overview of Model Selection ...

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