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- W1990443135 abstract "Biological systems are inherently noisy on a molecular scale. The problem of determining the true state of a system and characterizing the transitions between states whose observables are corrupted by noise is a canonical problem in statistics with a rich history. There has recently been a significant renaissance in the application of Bayesian Statistics (for instance variational Bayes and the use of the Bayesian Information Criterion) to analyze single-molecule biophysics and cell-biology problems. Although these Bayesian techniques regularize the model selection problem to prevent over-fitting through the introduction of a prior probability distribution, Bayesian techniques typically significantly underestimate the complexity that can be resolved by information-based and frequentist analyses and often require the introduction of ad hoc prior distributions. We demonstrate the application of information-based statistics (both canonical and novel) in the analyses of several distinct biological measurements from our laboratory ranging from single-molecule techniques including stoichiometry by bleaching and tether-particle motion to cell biology problems including characterizing cell motility. In each case we compare the resolution of the Bayesian and information-based approaches to demonstrate the increased resolving power of information-based techniques." @default.
- W1990443135 created "2016-06-24" @default.
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- W1990443135 date "2015-01-01" @default.
- W1990443135 modified "2023-09-27" @default.
- W1990443135 title "To Bayes, or Not to Bayes, Information is the Answer" @default.
- W1990443135 doi "https://doi.org/10.1016/j.bpj.2014.11.1008" @default.
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