Chairperson, Graduate Committee: Neil J. CornishMillhouse, Margaret Ann2018-10-022018-10-022018https://scholarworks.montana.edu/handle/1/14571After many years of preparation and anticipation, we are finally in the era of routine gravitational-wave detection. All of the detected signals so far have come from merging compact objects-- either black holes or neutron stars. These are signals for which we have very good waveform models, but there still exist other more poorly modeled sources as well as the possibility of completely new gravitational-wave sources. Because of this, it is important to have the ability to confidently detect gravitational-waves from a wide variety of sources. In this Thesis I will describe one particular algorithm used to detect and characterize gravitational-wave signals using Bayesian inference techniques, and minimal assumptions on the source of the gravitational wave. I will report on the methods and results of the implementation of this search in the first two observing runs of advanced LIGO. I will also discuss developments to this algorithm to improve waveform reconstruction, and target certain signals without using full waveform templates.enGravitational wavesBlack holes (Astronomy)Neutron starsAlgorithmsDissertationCopyright 2018 by Margaret Ann Millhouse