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Tag: regression

Is there a way to define a ‘heterogeneous’ kernel design to incorporate linear operators into the regression for GPflow (or GPytorch/GPy/…)?

I’m trying to perform a GP regression with linear operators as described in for example this paper by Särkkä: https://users.aalto.fi/~ssarkka/pub/spde.pdf In this example we can see from equation (8) that I need a different kernel function for the four covariance blocks (of training and test data) in the complete covariance matrix. This is definitely possible and valid, but I would

Polynomial fitting with equal number of data points and coefficients

I am currently experimenting with polynomial fitting using jupyter. The function below returns the least-square polynomial of degree m given the data points in xs with corresponding ys. Suppose I have the following six data points and fit a polynomial of degree 5: From my understanding, the resulting curve should pass through every single data point exactly (in fact, the

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