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.
標簽：
instantaneous
algorithm
Bayesian
Gaussian
上傳時間：
2013-12-19
上傳用戶：jjj0202
