GaussianPrior

class dysmalpy.parameters.GaussianPrior(center=0, stddev=1.0)

Bases: Prior

Object for gaussian priors

Parameters:

• center (float) – Mean of the Gaussian prior

• stddev (float) – Standard deviation of the Gaussian prior

Methods Summary

log_prior(param, **kwargs)	Returns the log value of the prior given the parameter value
prior_unit_transform(param, u, **kwargs)	Transforms a uniform random variable Uniform[0.,1.] to the prior distribution
sample_prior(param[, N])	Returns a random sample of parameter values distributed according to the prior

Methods Documentation

log_prior(param, **kwargs)

Returns the log value of the prior given the parameter value

Parameters:

param (DysmalParameter) – DysmalParameter object with which the prior is associated

Returns:

lprior – Log prior value calculated using pdf

Return type:

float

prior_unit_transform(param, u, **kwargs)

Transforms a uniform random variable Uniform[0.,1.] to the prior distribution

Parameters:

• param (DysmalParameter) – DysmalParameter object with which the prior is associated

• u (float or list-like) – Random uniform variable(s) drawn from Uniform[0.,1.]

Returns:

v – Transformation of the random uniform variable u to random value(s) drawn from the prior distribution.

Return type:

float or list-like

sample_prior(param, N=1, **kwargs)

Returns a random sample of parameter values distributed according to the prior

Parameters:

• param (DysmalParameter) – DysmalParameter object with which the prior is associated

• N (int, optional) – Size of random sample. Default is 1.

Returns:

rsamp – Random sample of parameter values

Return type:

float or array