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Authors: Christian Röver, Alexander Stroeer, Ed Bloomer, Nelson Christensen, James Clark, Martin Hendry, Chris Messenger, Renate Meyer, Matt Pitkin, Jennifer Toher, Richard Umstätter, Alberto Vecchio, John Veitch, Graham Woan

Date: 26 Jul 2007

Abstract: In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.

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