glearn.priors.Uniform.pdf

Uniform.pdf(x)

Probability density function of the prior distribution.

Parameters:

xfloat or array_like[float]

Input hyperparameter or an array of hyperparameters.

Returns:

pdffloat or array_like[float]

The probability density function of the input hyperparameter(s).

See also

glearn.priors.Prior.log_pdf()

glearn.priors.Uniform.pdf_jacobian()

glearn.priors.Uniform.pdf_hessian()

Notes

When an array of hyperparameters are given, it is assumed that prior for each hyperparameter is independent of others.

Examples

Create uniform prior in the interval \([0.2, 0.9]\):

>>> from glearn import priors
>>> prior = priors.Uniform(0.2, 0.9)

>>> # Evaluate PDF function at multiple locations
>>> t = [0, 0.5, 1]
>>> prior.pdf(t)
array([0.        , 1.42857143, 0.        ])