  We present a flexible code created for imaging from the bispectrum and
visibility-squared. By using a simulated annealing method, we limit the
probability of converging to local chi-squared minima as can occur when
traditional imaging methods are used on data sets with limited phase
information. We present the results of our code used on a simulated data set
utilizing a number of regularization schemes including maximum entropy. Using
the statistical properties from Monte-Carlo Markov chains of images, we show
how this code can place statistical limits on image features such as unseen
binary companions.
