UserDefinedDistribution

class cuqi.distribution.UserDefinedDistribution(dim=None, logpdf_func=None, gradient_func=None, sample_func=None, **kwargs)

Class to wrap user-defined logpdf, gradient, and/or sampling callable into CUQIpy Distribution.

Parameters:

• logpdf_func (Function evaluating log probability density function. Callable.)

• gradient_func (Function evaluating the gradient of the logpdf. Callable.)

• sample_func (Function drawing samples from distribution. Callable.)

Example

# Generate an i.i.d. n-dim Gaussian with zero mean and 2 variance.
mu1 = -1.0
std1 = 4.0
X = cuqi.distribution.Normal(mean=mu1, std=std1)
dim1 = 1
logpdf_func = lambda xx: -np.log(std1*np.sqrt(2*np.pi))-0.5*((xx-mu1)/std1)**2
sample_func = lambda : mu1 + std1*np.random.randn(dim1,1)
XU = cuqi.distribution.UserDefinedDistribution(dim=dim1, logpdf_func=logpdf_func, sample_func=sample_func)

__init__(dim=None, logpdf_func=None, gradient_func=None, sample_func=None, **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__([dim, logpdf_func, gradient_func, ...])	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()	Returns the conditioning variables of the distribution.
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.