BoundedSineGaussianPrior

class dysmalpy.parameters.BoundedSineGaussianPrior(center=0, stddev=1.0)[source]

Bases: Prior

Object for priors described by a bounded sine Gaussian distribution

Parameters:

center (float) – Central value of the Gaussian prior BEFORE applying sine function
stddev (float) – Standard deviation of the Gaussian prior AFTER applying sine function

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)[source]

Returns the log value of the prior given the parameter value

The parameter value is first checked to see if its within param.bounds. If so then a Gaussian distribution on the sine of the parameter is used to calculate the prior.

Parameters:: param (DysmalParameter) – DysmalParameter object with which the prior is associated
Returns:: lprior – Log prior value
Return type:: float

prior_unit_transform(param, u, **kwargs)[source]

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)[source]

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