pymc.EulerMaruyama

class pymc.EulerMaruyama(name, dt, sde_fn, *args, steps=None, **kwargs)

Stochastic differential equation discretized with the Euler-Maruyama method.

Parameters:

dtfloat

time step of discretization

sde_fncallable()

function returning the drift and diffusion coefficients of SDE

sde_parstuple

parameters of the SDE, passed as *args to sde_fn

init_distunnamed_distribution, optional

Scalar distribution for initial values. Distributions should have shape (*shape[:-1]). If not, it will be automatically resized. Defaults to pm.Normal.dist(0, 100, shape=…).

Warning

init_dist will be cloned, rendering it independent of the one passed as input.

Methods

EulerMaruyama.dist(dt, sde_fn, sde_pars, *)

Creates a tensor variable corresponding to the cls distribution.