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Thursday, March 5 • 15:15 - 17:15
Poster: 'Fast Step Transition and State Identification (STaSI) for Discrete Single-Molecule Data Analysis,' Bo Shuang, Rice University

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Interpretation of noisy signal is critical for early disaster detection, fast dynamic exploration, weak signal transportation, etc.. Even though denoising techniques based on different models are well-developed, model selection is still subjective and experience-based. To solve this problem, we introduce an objective model selection algorithm for piecewise constant signal based on Occam's razor: "the fewer assumptions that are made, the better". According to minimum description length principle (one formalization of Occam's razor), signal interpretation based on different models require different amount of storage space in a computer, and the model with the minimum storage space should be the closest approximation to the mechanism behind the signal. Our algorithm provides comprehensive, objective analysis of multiple data sets requiring few user inputs about the underlying physical models and is faster and more precise in determining the number of states than other established and cutting-edge methods, and thus can be applied to a broad range of signals.

Speakers

Thursday March 5, 2015 15:15 - 17:15 CST
BioScience Research Collaborative 6500 Main Street, Houston, Tx 77005