  "Glitches" -- transient noise artifacts in the data collected by
gravitational wave interferometers like LIGO and Virgo -- are an ever-present
obstacle for the search and characterization of gravitational wave signals.
With some having morphology similar to high mass, high mass-ratio, and
extreme-spin binary black hole events, they limit sensitivity to such sources.
They can also act as a contaminant for all sources, requiring targeted
mitigation before astrophysical inferences can be made. We propose a data
driven, parametric model for frequently encountered glitch types using
probabilistic principal component analysis. As a noise analog of parameterized
gravitational wave signal models, it can be easily incorporated into existing
search and detector characterization techniques. We have implemented our
approach with the open source glitschen package. Using LIGO's currently most
problematic glitch types, the 'blip' and 'tomte', we demonstrate that
parametric models of modest dimension can be constructed and used for effective
mitigation in both frequentist and Bayesian analyses.
