  Gravitational wave data from ground-based detectors is dominated by
instrument noise. Signals will be comparatively weak, and our understanding of
the noise will influence detection confidence and signal characterization.
Mis-modeled noise can produce large systematic biases in both model selection
and parameter estimation. Here we introduce a multi-component, variable
dimension, parameterized model to describe the Gaussian-noise power spectrum
for data from ground-based gravitational wave interferometers. Called
BayesLine, the algorithm models the noise power spectral density using cubic
splines for smoothly varying broad-band noise and Lorentzians for narrow-band
line features in the spectrum. We describe the algorithm and demonstrate its
performance on data from the fifth and sixth LIGO science runs. Once fully
integrated into LIGO/Virgo data analysis software, BayesLine will produce
accurate spectral estimation and provide a means for marginalizing inferences
drawn from the data over all plausible noise spectra.
