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Function parametrization: Difference between revisions
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== Alternatives == | == Alternatives == | ||
* Neural networks. Like with FP, most of the computational effort is concentrated in an analysis phase (network ''training''), before the actual application to data. Therefore, this method is fast and suited for real-time applications. With FP, non-linear dependencies are limited by the degree of the polynomial expansions used, whereas neural networks allow more general non-linear dependencies, in principle. | |||
* Neural networks. Like with FP, most of the computational effort is concentrated in an analysis phase (network ''training''), before the actual application to data. Therefore, this method is fast and suited for real-time applications. | * [[Bayesian data analysis]], which allows non-Gaussian error distributions and complex data dependencies. A very powerful method but not fast due to the need for maximization (not suited for real-time applications). | ||
== References == | == References == | ||
<references /> | <references /> | ||