pybop.parameters.priors#

Classes#

`BasePrior`	A base class for defining prior distributions.
`Exponential`	Represents an exponential distribution with a specified scale parameter.
`Gaussian`	Represents a Gaussian (normal) distribution with a given mean and standard deviation.
`Uniform`	Represents a uniform distribution over a specified interval.

Module Contents#

class pybop.parameters.priors.BasePrior[source]#

A base class for defining prior distributions.

This class provides a foundation for implementing various prior distributions. It includes methods for calculating the probability density function (PDF), log probability density function (log PDF), and generating random variates from the distribution.

prior#

The underlying continuous random variable distribution.

Type:: scipy.stats.rv_continuous

loc#

The location parameter of the distribution.

Type:: float

scale#

The scale parameter of the distribution.

Type:: float

__repr__()[source]#: Returns a string representation of the object.

logpdf(x)[source]#

Calculates the logarithm of the probability density function of the distribution at x.

Parameters:: x (float) – The point(s) at which to evaluate the log pdf.
Returns:: The logarithm of the probability density function value at x.
Return type:: float

pdf(x)[source]#

Calculates the probability density function (PDF) of the distribution at x.

Parameters:: x (float) – The point(s) at which to evaluate the pdf.
Returns:: The probability density function value at x.
Return type:: float

rvs(size=1, random_state=None)[source]#

Generates random variates from the distribution.

Parameters:

size (int) – The number of random variates to generate.
random_state (int, optional) – The random state seed for reproducibility. Default is None.

Returns:

An array of random variates from the distribution.

Return type:

array_like

Raises:

ValueError – If the size parameter is negative.

property mean[source]#

Get the mean of the distribution.

Returns:: The mean of the distribution.
Return type:: float

property sigma[source]#

Get the standard deviation of the distribution.

Returns:: The standard deviation of the distribution.
Return type:: float

class pybop.parameters.priors.Exponential(scale, loc=0, random_state=None)[source]#

Bases: BasePrior

Represents an exponential distribution with a specified scale parameter.

This class provides methods to calculate the pdf, the log pdf, and to generate random variates from the distribution.

Parameters:: scale (float) – The scale parameter (lambda) of the exponential distribution.

class pybop.parameters.priors.Gaussian(mean, sigma, random_state=None)[source]#

Bases: BasePrior

Represents a Gaussian (normal) distribution with a given mean and standard deviation.

This class provides methods to calculate the probability density function (pdf), the logarithm of the pdf, and to generate random variates (rvs) from the distribution.

Parameters:

mean (float) – The mean (mu) of the Gaussian distribution.
sigma (float) – The standard deviation (sigma) of the Gaussian distribution.

class pybop.parameters.priors.Uniform(lower, upper, random_state=None)[source]#

Bases: BasePrior

Represents a uniform distribution over a specified interval.

This class provides methods to calculate the pdf, the log pdf, and to generate random variates from the distribution.

Parameters:

lower (float) – The lower bound of the distribution.
upper (float) – The upper bound of the distribution.

property mean[source]#
Returns the mean of the distribution.

property sigma[source]#
Returns the standard deviation of the distribution.