pymc.MvGaussianRandomWalk#

class pymc.MvGaussianRandomWalk(*args, **kwargs)[source]#

Multivariate Random Walk with Normal innovations

Parameters
mu: tensor

innovation drift, defaults to 0.0

cov: tensor

pos def matrix, innovation covariance matrix

tau: tensor

pos def matrix, inverse covariance matrix

chol: tensor

Cholesky decomposition of covariance matrix

init: distribution

distribution for initial value (Defaults to Flat())

Notes

Only one of cov, tau or chol is required.

Methods

MvGaussianRandomWalk.__init__([mu, cov, ...])

MvGaussianRandomWalk.dist(*args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

MvGaussianRandomWalk.logp(x)

Calculate log-probability of Multivariate Gaussian Random Walk distribution at specified value.

MvGaussianRandomWalk.random(**kwargs)

Attributes

rv_class

rv_op