sigma_0 :
A simple constant squared value which is broadcasted across kernel.
In the case of GPR – standard deviation of the Gaussian prior
probability Normal(0, sigma_0**2) of the intercept of the linear
regression. (Default: 1.0)
Sigma_p :
A generic scalar or vector, or diagonal matrix to scale all
dimensions or associate different scaling to each dimensions while
computing te kernel matrix: k(x_A,x_B) = x_A^\top \Sigma_p
x_B + \sigma_0^2. In the case of GPR – a scalar or a diagonal of
covariance matrix of the Gaussian prior probability Normal(0,
Sigma_p) on the weights of the linear regression. (Default: 1.0)
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition
to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
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