Lognormal#

class cuqi.distribution.Lognormal(mean, cov, is_symmetric=False, **kwargs)#

Multivariate Lognormal distribution

Parameters:

mean (np.ndarray) – Mean of the normal distribution used to define the lognormal distribution
cov (np.ndarray) – Covariance matrix of the normal distribution used to define the lognormal distribution

Example

# Generate a lognormal distribution
mean = np.array([1.5,1])
cov = np.array([[3, 0],[0, 1]])
x = cuqi.distribution.Lognormal(mean, cov)
samples = x.sample(10000)
samples.hist_chain(1, bins=70)

__init__(mean, cov, 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, cov[, 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)	Evaluate the log probability density function of the distribution.
`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.
`is_cond`	Returns True if instance (self) is a conditional distribution.
`name`	Name of the random variable associated with the density.