  We present a method that assesses the theoretical detection limit of a
Bayesian Markov chain Monte Carlo search for a periodic gravitational wave
signal emitted by a neutron star. Inverse probability yields an upper limit
estimate for the strength when a signal could not be detected in an observed
data set. The proposed method is based on Bayesian model comparison that
automatically quantifies Occam s Razor. It limits the complexity of a model by
favoring the most parsimonious model that explains the data. By comparing the
model with a signal from a pulsar to the null model that assumes solely noise,
we derive the detection probability and an estimate for the upper limit that a
search, for example, for a narrow-band emission for SN1987a, might yield on
data at the sensitivity of LIGO data for an observation time of one year.

General Relativity and Quantum Cosmology