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A new model-based approach for estimating the parameters of an arbitrary transformation between two discrete-time sequences is introduced. One sequence is interpreted as part of a nonlinear measurement equation, the other sequence is typically measured sequentially. Based on every measured value, the probability density function of the parameters is updated using a Bayesian approach. For the evolution of the system over time, a system equation is included. The new approach provides a high update rate for the desired parameters up to the sampling rate with high accuracy. It is demonstrated for source localization of a speaker, where the parameters describe the position of the source.