This a Bayesian ICA algorithm FOR the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
This software was done in part FOR a textbook on AI I ve written called _The Basis of AI_ (tentative title, subject to change but not if I get my way). FOR details see: http://www.mcs.com/~drt/basisofai.html
