TruncatedNormal#

class cuqi.distribution.TruncatedNormal(mean=None, std=None, low=-inf, high=inf, is_symmetric=False, **kwargs)#

Truncated Normal probability distribution.

Generates instance of cuqi.distribution.TruncatedNormal. It allows the user to specify upper and lower bounds on random variables represented by a Normal distribution. This distribution is suitable for a small dimension setup (e.g. `dim`=3 or 4). Using TruncatedNormal Distribution with a larger dimension can lead to a high rejection rate when used within MCMC samplers.

The variables of this distribution are iid.

Parameters:

mean (float or array_like of floats) – mean of distribution
std (float or array_like of floats) – standard deviation
low (float or array_like of floats) – lower bound of the distribution
high (float or array_like of floats) – upper bound of the distribution

Example

#Generate Normal with mean 0, standard deviation 1 and bounds [-2,2]
p = cuqi.distribution.TruncatedNormal(mean=0, std=1, low=-2, high=2)
samples = p.sample(5000)

__init__(mean=None, std=None, low=-inf, high=inf, is_symmetric=False, **kwargs)#

Initialize the core properties of the distribution.

Parameters:

name (str, default None) – Name of distribution.
geometry (Geometry, default _DefaultGeometry (or None)) – Geometry of distribution.
is_symmetric (bool, default None) – Indicator if distribution is symmetric.

Methods

`__init__`([mean, std, low, high, is_symmetric])	Initialize the core properties of the distribution.
`disable_FD`()	Disable finite difference approximation for logd gradient.
`enable_FD`([epsilon])	Enable finite difference approximation for logd gradient.
`get_conditioning_variables`()	Return the conditioning variables of this distribution (if any).
`get_mutable_variables`()	Return any public variable that is mutable (attribute or property) except those in the ignore_vars list
`get_parameter_names`()	Returns the names of the parameters that the density can be evaluated at or conditioned on.
`gradient`(args, *kwargs)	Returns the gradient of the log density at x.
`logd`(args, *kwargs)	Evaluate the un-normalized log density function of the distribution.
`logpdf`(x)	Computes the unnormalized logpdf at the given values of x.
`pdf`(x)	Evaluate the log probability density function of the distribution.
`sample`([N])	Sample from the distribution.
`to_likelihood`(data)	Convert conditional distribution to a likelihood function given observed data

Attributes

`FD_enabled`	Returns True if finite difference approximation of the logd gradient is enabled.
`FD_epsilon`	Spacing for the finite difference approximation of the logd gradient.
`dim`	Return the dimension of the distribution based on the geometry.
`geometry`	Return the geometry of the distribution.
`high`	Higher bound of the distribution
`is_cond`	Returns True if instance (self) is a conditional distribution.
`low`	Lower bound of the distribution
`mean`	Mean of the distribution
`name`	Name of the random variable associated with the density.
`std`	Std of the distribution