# 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]) `squeeze`(axis) Squeeze a plate axis from the distribution