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[Bio-Recognize] icaML DL : 0: 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.
Date : 2008-10-13 Size : 7.48kb User : 陈互
[Bio-Recognize] icaML DL : 0: 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.
Date : 2026-01-01 Size : 7kb User : 陈互
[Bio-Recognize] codes_FITM DL : 0: 进一步的属性和快速迭代截断算术平均滤波的实现，这篇文章的matlab代码实现-Further properties and fast iteration truncated arithmetic mean filter implementation, this article matlab code implementation
Date : 2026-01-01 Size : 69kb User : liwei