hopsy.Mixture

class hopsy.Mixture(self, components, weights=[1, ..., 1])

The Mixture is a weighted sum of \(n\) components, so its unnormalized density is given as

\[f(x) = \sum_{i=1}^n w_i f_i(x)\]

The components may be arbitrary python objects implementing the methods as required in a hopsy.PyModel

Parameters:

• components (list[object]) – The Mixture’s model components.

• weights (list[float]) – Component weights. If none are given, they will be assumed to be all 1.

Attributes

components	A list of component weights, which has to match the number of components.
weights	A list of model components, where every components is supposed to be a Python object implementing hopsy.Model or being wrapped inside hopsy.PyModel.

Methods

compute_expected_fisher_information(self, x)	deprecated:: 1.4
compute_log_likelihood_gradient(self, x)	deprecated:: 1.4
compute_negative_log_likelihood(self, x)	deprecated:: 1.4
log_curvature(self x)	This method is not implemented, as there exists no closed-form solution to computing the log curvature (typically defined as the expected Fisher information) of a general mixture model.
log_density(self, x)	Computes the log probability density of the model components
log_gradient(self, x)	Computes the gradient of the logarithm of the weighted sum of the probability density functions of the model components