  The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will
begin operation in the coming years, with compact binary coalescence events a
likely source for the first detections. The gravitational waveforms emitted
directly encode information about the sources, including the masses and spins
of the compact objects. Recovering the physical parameters of the sources from
the GW observations is a key analysis task. This work describes the
LALInference software library for Bayesian parameter estimation of compact
binary signals, which builds on several previous methods to provide a
well-tested toolkit which has already been used for several studies. We show
that our implementation is able to correctly recover the parameters of compact
binary signals from simulated data from the advanced GW detectors. We
demonstrate this with a detailed comparison on three compact binary systems: a
binary neutron star, a neutron star black hole binary and a binary black hole,
where we show a cross-comparison of results obtained using three independent
sampling algorithms. These systems were analysed with non-spinning, aligned
spin and generic spin configurations respectively, showing that consistent
results can be obtained even with the full 15-dimensional parameter space of
the generic spin configurations. We also demonstrate statistically that the
Bayesian credible intervals we recover correspond to frequentist confidence
intervals under correct prior assumptions by analysing a set of 100 signals
drawn from the prior. We discuss the computational cost of these algorithms,
and describe the general and problem-specific sampling techniques we have used
to improve the efficiency of sampling the compact binary coalescence parameter
space.
