Coherent Bayesian inference on compact binary inspirals 
using a network of interferometric gravitational wave detectors

(Christian Roever, Renate Meyer and Nelson Christensen)


Presented in this paper is the description of a Markov chain Monte 
Carlo (MCMC) routine for conducting coherent parameter estimation 
for interferometric gravitational wave observations of an inspiral 
of binary compact objects using multiple detectors. Data from 
several interferometers are processed, and all nine parameters 
(ignoring spin) associated with the binary system are inferred, 
including the distance to the source, the masses, and the location 
on the sky. The data is matched with time-domain inspiral templates 
that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. 
We designed and tuned an MCMC sampler so that it is able to 
efficiently find the posterior mode(s) in the parameter space and 
perform the stochastic integration necessary for inference within 
a Bayesian framework. Examples are given for simulated signals and 
data as seen by the LIGO and Virgo detectors.