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Explore Stack InternalI am having a bit of confusionconfused. Why are gaussianGaussian processes called non parametric models?
They do assume that the functional values, or a subset of them, have a gaussianGaussian prior with mean 0 and covariance function given as the kernel function. These kernel functions themselves have some parameters (hyperparametersi.e., hyperparameters).
So why are they called non parametric models?
I am having a bit of confusion. Why are gaussian processes called non parametric models?
They do assume that the functional values or a subset of them have a gaussian prior with mean 0 and covariance function given as the kernel function. These kernel functions themselves have some parameters(hyperparameters).
So why are they called non parametric models?
I am a bit confused. Why are Gaussian processes called non parametric models?
They do assume that the functional values, or a subset of them, have a Gaussian prior with mean 0 and covariance function given as the kernel function. These kernel functions themselves have some parameters (i.e., hyperparameters).
So why are they called non parametric models?
I am having a bit of confusion. Why are gaussian processes called non parametric models?
They do assume that the functional values or a subset of them have a gaussian prior with mean 0 and covariance function given as the kernel function. These kernel functions themselves have some parameters(hyperparameters).
So why are they called non parametric models?
