bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution¶

class bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution(categories, states)[source]¶

Class for the VMP formulas of categorical Markov chain variables.

__init__(categories, states)[source]¶

Create VMP formula node for a categorical variable

categories is the total number of categories. states is the length of the chain.

Methods

`__init__`(categories, states)	Create VMP formula node for a categorical variable
`compute_cgf_from_parents`(u_p0, u_P)	Compute $\mathrm{E}_{q(p)}[g(p)]$
`compute_fixed_moments_and_f`(x[, mask])	Compute the moments and $f(x)$ for a fixed value.
`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 the message to a parent node.
`compute_moments_and_cgf`(phi[, mask])	Compute the moments and $g(\phi)$ .
`compute_phi_from_parents`(u_p0, u_P[, mask])	Compute the natural parameter vector given parent moments.
`compute_weights_to_parent`(index, weights)	Maps the mask to the plates of a parent.
`plates_from_parent`(index, plates)	Resolve the plate mapping from a parent.
`plates_to_parent`(index, plates)	Resolves the plate mapping to a parent.
`random`(*phi[, plates])	Draw a random sample from the distribution.
`squeeze`(axis)	Squeeze a plate axis from the distribution

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