Generating Sparse Stochastic Processes

This library implements a method to generate sparse stochastic processes as defined here.

Requirements

Before running the code please make sure that all the necessary requirements are installed with the command

pip install -r requirements.txt

Tutorial

A detailed example is provided in the Jupyter Notebook Example.ipynb.

Here is a short example on how to use the library to simulate Brownian motion :

from lib.lspline import L_spline
from lib.loperator import Operator
from lib.white_noise import white_noise

w = white_noise('gaussian', params=(0,1))
L = Operator([1, 0])
s = L_spline(L, w)

s.set_lambda(lmda = 100)

s.sample(T=1)
grid_values = s.get_grid_samples(T=1, step=0.01)