Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

In this paper we present a description of a Bayesian analysis framework for use with interferometric gravitational radiation data in search of binary neutron star inspiral signals. Five parameters are investigated, and the information extracted from the data is illustrated and quantified. The posterior integration is carried out using Markov chain Monte Carlo (MCMC) methods. Implementation details include the use of importance resampling for improved convergence and informative priors reflecting the conditions expected for realistic measurements. An example is presented from an application using realistic, albeit fictitious, data. We expect that these parameter estimation techniques will prove useful at the end of a binary inspiral detection pipeline for interferometric detectors like LIGO or Virgo.