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Generalized filtering is a generic Bayesian filtering scheme for nonlinear state-space models.〔K Friston, K Stephan, B Li, and J. Daunizeau, "(Generalised Filtering )," Mathematical Problems in Engineering, vol. vol., 2010, p. 621670, 2010.〕 It is based on a variational principle of least action, formulated in generalized coordinates of motion.〔B Balaji and K Friston, "(Bayesian state estimation using generalized coordinates )," Proc. SPIE, p. 80501Y , 2011〕 Generalized filtering furnishes posterior densities over hidden states (and parameters) generating observed data using a generalized gradient descent on variational free energy, under the Laplace assumption. Unlike classical (e.g., Kalman-Bucy or particle) filtering, generalized filtering eschews Markovian assumptions about random fluctuations. Furthermore, it operates online, assimilating data to approximate the posterior density over unknown quantities, without the need for a backward pass. Special cases include variational filtering,〔 dynamic expectation maximization〔 and generalized predictive coding. == Definition == Definition: Generalized filtering rests on the tuple : * ''A sample space'' from which random fluctuations are drawn * ''Control states'' – that act as external causes, input or forcing terms * ''Hidden states'' – that cause sensor states and depend on control states * ''Sensor states'' – a probabilistic mapping from hidden and control states * ''Generative density'' – over sensory, hidden and control states under a generative model * ''Variational density'' – over hidden and control states with mean Here ~ denotes a variable in generalized coordinates of motion: 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Generalized filtering」の詳細全文を読む スポンサード リンク
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