  The Laser Interferometer Space Antenna will be able to detect the inspiral
and merger of Super Massive Black Hole Binaries (SMBHBs) anywhere in the
Universe. Standard matched filtering techniques can be used to detect and
characterize these systems. Markov Chain Monte Carlo (MCMC) methods are ideally
suited to this and other LISA data analysis problems as they are able to
efficiently handle models with large dimensions. Here we compare the posterior
parameter distributions derived by an MCMC algorithm with the distributions
predicted by the Fisher information matrix. We find excellent agreement for the
extrinsic parameters, while the Fisher matrix slightly overestimates errors in
the intrinsic parameters.
