pymc.MvStudentTRandomWalk#

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

Multivariate Random Walk with StudentT innovations

Parameters
nu: degrees of freedom
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())

Methods

MvStudentTRandomWalk.__init__(nu, *args, ...)

MvStudentTRandomWalk.dist(*args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

MvStudentTRandomWalk.logp(x)

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

MvStudentTRandomWalk.random(**kwargs)

Attributes

rv_class

rv_op