  The Mock LISA Data Challenge is a worldwide effort to solve the LISA data
analysis problem. We present here our results for the Massive Black Hole Binary
(BBH) section of Round 1. Our results cover Challenge 1.2.1, where the
coalescence of the binary is seen, and Challenge 1.2.2, where the coalescence
occurs after the simulated observational period. The data stream is composed of
Gaussian instrumental noise plus an unknown BBH waveform. Our search algorithm
is based on a variant of the Markov Chain Monte Carlo method that uses
Metropolis-Hastings sampling and thermostated frequency annealing. We present
results from the training data sets and the blind data sets. We demonstrate
that our algorithm is able to rapidly locate the sources, accurately recover
the source parameters, and provide error estimates for the recovered
parameters.
