| Summary: | With the wide range of fields engaging in signal processing research, many
methods do not receive adequate dissemination across disciplines due to
differences in jargon, notation, and level of rigor. In this thesis, I attempt to
bridge this gap by applying two statistical techniques originating in signal
processing to fields for which they were not originally intended. Firstly,
I employ particle filters, a tool used for state estimation in the physics
signal processing world, for the task of prior sensitivity analysis and cross
validation in Bayesian statistics. Secondly, I demonstrate the application of
support vector forecasters, a tool used for forecasting in the machine learning
signal processing world, to the field of structural health monitoring.
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