pymc.GaussianRandomWalk#

class pymc.GaussianRandomWalk(*args, steps=None, **kwargs)[source]#

Random Walk with Normal innovations

Parameters
mutensor_like of float

innovation drift, defaults to 0.0

sigmatensor_like of float, optional

sigma > 0, innovation standard deviation, defaults to 1.0

init_distunnamed distribution

Univariate distribution of the initial value, created with the .dist() API.

Warning

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

stepsint, optional

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

Methods

GaussianRandomWalk.__init__(*args, **kwargs)

GaussianRandomWalk.dist([mu, sigma, ...])

Creates a tensor variable corresponding to the cls distribution.

GaussianRandomWalk.logp(mu, sigma, ...)

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

GaussianRandomWalk.moment(size, mu, sigma, ...)

Attributes

rv_class

rv_op