  A central challenge in Gravitational Wave Astronomy is identifying weak
signals in the presence of non-stationary and non-Gaussian noise. The
separation of gravitational wave signals from noise requires good models for
both. When accurate signal models are available, such as for binary Neutron
star systems, it is possible to make robust detection statements even when the
noise is poorly understood. In contrast, searches for "un-modeled" transient
signals are strongly impacted by the methods used to characterize the noise.
Here we take a Bayesian approach and introduce a multi-component, variable
dimension, parameterized noise model that explicitly accounts for
non-stationarity and non-Gaussianity in data from interferometric gravitational
wave detectors. Instrumental transients (glitches) and burst sources of
gravitational waves are modeled using a Morlet-Gabor continuous wavelet frame.
The number and placement of the wavelets is determined by a trans-dimensional
Reversible Jump Markov Chain Monte Carlo algorithm. The Gaussian component of
the noise and sharp line features in the noise spectrum are modeled using the
BayesLine algorithm, which operates in concert with the wavelet model.
