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Single Molecule Data Analysis: An Introduction
Stockholm University, Faculty of Science, Department of Mathematics. Hokkaido University, Japan.
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2017 (English)In: Advances in Chemical Physics / [ed] Stuart A. Rice, Aaron R. Dinner, Hoboken, USA: John Wiley & Sons, 2017, p. 205-305Chapter in book (Refereed)
Abstract [en]

This chapter considers statistical data-driven analysis methods, and focuses on parametric as well as more recent information theoretic and nonparametric statistical approaches to biophysical data analysis with an emphasis on single-molecule applications. It then reviews simpler parametric approaches starting from an assumed model with unknown parameters. Model selection criteria are widely used in biophysical data analysis from image deconvolution to single-molecule step detection and continue to be developed by statisticians. The goal of successful model selection criteria is to pick models whose complexity is penalized, in a principled fashion, to avoid overfitting and that convincingly fit the data provided (the training set). The chapter summarizes both information theoretic as well as Bayesian model selection criteria. Finally, the chapter discusses efforts to use information theory in experimental design and ends with some considerations on the broader applicability of information theory.

Place, publisher, year, edition, pages
Hoboken, USA: John Wiley & Sons, 2017. p. 205-305
Series
Advances in Chemical Physics, ISSN 0065-2385 ; 162
Keywords [en]
Bayesian model selection criteria, Bayesian nonparametrics, Bayesian parametric approaches, Frequentist parametric approaches, information theory, single molecule data analysis, single-molecule applications
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-151938DOI: 10.1002/9781119324560.ch4ISI: 000432397200005ISBN: 9781119324577 (print)ISBN: 9781119324560 (electronic)OAI: oai:DiVA.org:su-151938DiVA, id: diva2:1176194
Available from: 2018-01-20 Created: 2018-01-20 Last updated: 2018-06-25Bibliographically approved

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