Forecast ergodicity and instantaneous active information.

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dc.contributor.advisorMarks, Robert J., II (Robert Jackson), 1950-
dc.creatorAmigo Galan, Glauco A., 1984-
dc.date.accessioned2023-11-07T14:21:13Z
dc.date.available2023-11-07T14:21:13Z
dc.date.created2023-05
dc.date.issuedMay 2023
dc.date.submittedMay 2023
dc.date.updated2023-11-07T14:21:13Z
dc.description.abstractThis dissertation introduces two new information theory tools for machine learning. Forecast ergodicity models prediction using algorithmic information theory, stating that future data can be forecasted if it has the same structure as past data. The novelty of this approach is that it is model-free, based only on data. The other tool is instantaneous active information. It measures the active information at each step of iterative searches that are assisted by oracles. The instantaneous active information characterizes the difficulty of each subsequent step of a search. Knowing the difficulty of each search step helps to make real-time decisions about conflicting goals. As a demonstration, this search analysis is applied to a phased array optimization problem where there exists a tradeoff between the time devoted to parameter optimization and signal transmission.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/2104/12486
dc.language.isoEnglish
dc.rights.accessrightsNo access – contact librarywebmaster@baylor.edu
dc.titleForecast ergodicity and instantaneous active information.
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentBaylor University. Dept. of Electrical & Computer Engineering.
thesis.degree.grantorBaylor University
thesis.degree.namePh.D.
thesis.degree.programElectrical & Computer Engineering
thesis.degree.schoolBaylor University

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