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In this paper, the first algorithm for learning hybrid Bayesian Networks with Gaussian mixture and Dirac mixture conditional densities from data given their structure is presented. The mixture densities to be learned allow for nonlinear dependencies between the variables and exact closed-form inference. For learning the network's parameters, an incremental gradient ascent algorithm is derived. Analytic...
Theoretical analysis of the unbiased plain gradient (UPG) algorithm for a constrained adaptive IIR notch filter (ANF) is presented in this paper. The difference equations for the convergence to the mean of the estimated parameter, estimation bias, and stability bound are derived. It is shown that the UPG provides bias smaller that that of the PG. Computer simulations have been treated to corroborate...
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