  Bayesian model selection provides a powerful and mathematically transparent
framework to tackle hypothesis testing, such as detection tests of
gravitational waves emitted during the coalescence of binary systems using
ground-based laser interferometers. Although its implementation is
computationally intensive, we have developed an efficient probabilistic
algorithm based on a technique known as nested sampling that makes Bayesian
model selection applicable to follow-up studies of candidate signals produced
by on-going searches of inspiralling compact binaries. We discuss the
performance of this approach, in terms of "false alarm rate" and "detection
probability" of restricted second post-Newtonian inspiral waveforms from
non-spinning compact objects in binary systems. The results confirm that this
approach is a viable tool for detection tests in current searches for
gravitational wave signals.
