  Bayesian parameter estimation is fast becoming the language of
gravitational-wave astronomy. It is the method by which gravitational-wave data
is used to infer the sources' astrophysical properties. We introduce a
user-friendly Bayesian inference library for gravitational-wave astronomy,
Bilby. This python code provides expert-level parameter estimation
infrastructure with straightforward syntax and tools that facilitate use by
beginners. It allows users to perform accurate and reliable gravitational-wave
parameter estimation on both real, freely-available data from LIGO/Virgo, and
simulated data. We provide a suite of examples for the analysis of compact
binary mergers and other types of signal model including supernovae and the
remnants of binary neutron star mergers. These examples illustrate how to
change the signal model, how to implement new likelihood functions, and how to
add new detectors. Bilby has additional functionality to do population studies
using hierarchical Bayesian modelling. We provide an example in which we infer
the shape of the black hole mass distribution from an ensemble of observations
of binary black hole mergers.
