pymc.MvGaussianRandomWalk

class pymc.MvGaussianRandomWalk(name, *args, **kwargs)

Random Walk with Multivariate Normal innovations

Parameters

mu: tensor_like of float

innovation drift

cov: tensor_like of float

pos def matrix, innovation covariance matrix

tau: tensor_like of float

pos def matrix, inverse covariance matrix

chol: tensor_like of float

Cholesky decomposition of covariance matrix

lowerbool, default=True

Whether the cholesky fatcor is given as a lower triangular matrix.

init_dist: distribution

Unnamed multivariate distribution of the initial value. Unnamed refers to distributions

created with the .dist() API.

Warning

init_dist will be cloned, rendering them independent of the ones passed as input.

stepsint, optional

Number of steps in Random Walk (steps > 0). Only needed if shape is not provided.

Notes

Only one of cov, tau or chol is required.

Methods

MvGaussianRandomWalk.__init__(*args, **kwargs)
MvGaussianRandomWalk.dist(*args, **kwargs)
MvGaussianRandomWalk.get_dists(mu, *[, cov, ...])
MvGaussianRandomWalk.mro(/)	Return a type's method resolution order.
MvGaussianRandomWalk.register(subclass)	Register a virtual subclass of an ABC.