pymc.MvGaussianRandomWalk#
- class pymc.MvGaussianRandomWalk(name, *args, **kwargs)[source]#
Random Walk with Multivariate Normal innovations
- Parameters
- mutensor_like of
float innovation drift
- covtensor_like of
float pos def matrix, innovation covariance matrix
- tautensor_like of
float pos def matrix, inverse covariance matrix
- choltensor_like of
float Cholesky decomposition of covariance matrix
- lowerbool, default=True
Whether the cholesky fatcor is given as a lower triangular matrix.
- init_distunnamed_distribution
Unnamed multivariate distribution of the initial value.
Warning
init_dist will be cloned, rendering them independent of the ones passed as input.
- steps
int, optional Number of steps in Random Walk (steps > 0). Only needed if shape is not provided.
- mutensor_like of
Notes
Only one of cov, tau or chol is required.
Methods
MvGaussianRandomWalk.__init__(*args, **kwargs)MvGaussianRandomWalk.dist(*args, **kwargs)MvGaussianRandomWalk.get_dists(mu, *[, cov, ...])Return a type's method resolution order.
MvGaussianRandomWalk.register(subclass)Register a virtual subclass of an ABC.