# bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution¶

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

Implementation of VMP formulas for Gaussian Markov chain

The log probability density function of the prior:

Todo

Fix inputs and their weight matrix in the equations.

For simplicity, and are assumed not to depend on in the above equation, but this distribution class supports that dependency. One only needs to do the following replacements in the equations: and , where .

The log probability denisty function of the posterior approximation:

__init__(N, D)

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(N, D) Initialize self. compute_cgf_from_parents(u_mu_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_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])