glearn.priors.Normal.suggest_hyperparam

Normal.suggest_hyperparam(positive=False)

Find an initial guess for the hyperparameters based on the peaks of the prior distribution.

Parameters:

positivebool, default=False

If True, it suggests a positive hyperparameter. This is used for instance if the suggested hyperparameter is used for the scale parameter which should always be positive.

Returns:

hyperparamfloat or numpy.array[float]

A feasible guess for the hyperparameter. The output is either a scalar or an array of the same size as the input parameters of the distribution.

See also

glearn.GaussianProcess

Notes

For the normal distribution \(\mathcal{N}(\mu, \sigma^2)\), suggested hyperparameter is the mean \(\mu\).

If the input arguments mean is given as an \(\boldsymbol{\mu} = (\mu_1, \dots, \mu_p)\), then the output of this function is the array \(\boldsymbol{\mu}\).

The suggested hyperparameters can be used as initial guess for the optimization of the posterior functions when used with this prior.

Examples

Create the normal distribution \(\mathcal{N}(1, 3^2)\):

>>> from glearn import priors
>>> prior = priors.Normal(1, 3)

>>> # Find a feasible hyperparameter value
>>> prior.suggest_hyperparam()
array([1.])

The above value is the mean of the distribution.