bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution¶

class bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution(N, D)[source]¶

Sub-classes implement distribution specific computations.

__init__(N, D)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(N, D)	Initialize self.
`compute_cgf_from_parents`(u_mu, u_Lambda, …)	Compute CGF using the moments of the parents.
`compute_fixed_moments_and_f`(x[, mask])	Compute u(x) and f(x) for given x.
`compute_gradient`(g, u, phi)	Compute the standard gradient with respect to the natural parameters.
`compute_logpdf`(u, phi, g, f, ndims)	Compute E[log p(X)] given E[u], E[phi], E[g] and E[f].
`compute_message_to_parent`(parent, index, u, …)	Compute a message to a parent.
`compute_moments_and_cgf`(phi[, mask])	Compute the moments and the cumulant-generating function.
`compute_phi_from_parents`(u_mu, u_Lambda, …)	Compute the natural parameters using parents’ moments.
`compute_rotation_bound`(u, u_mu_Lambda, u_A_V, R)
`compute_weights_to_parent`(index, weights)	Maps the mask to the plates of a parent.
`plates_from_parent`(index, plates)	Compute the plates using information of a parent node.
`plates_to_parent`(index, plates)	Computes the plates of this node with respect to a parent.
`random`(*params[, plates])	Draw a random sample from the distribution.
`rotate`(u, phi, R[, inv, logdet])