Gaussian Process RBF#

Simulate and visualise paths

# Author: Dialid Santiago <d.santiago@outlook.com>
# License: MIT
# Description: Simulate and visualise Radial Basis Function (RBF) Gaussian Process

from aleatory.processes import GPRBF
from aleatory.styles import qp_style

qp_style()  # Use quant-pastel-style

process = GPRBF(length_scale=0.1, sigma=1.0, T=1.0)
fig = process.plot_paths_and_kernel(n=100, N=5, matrix_shape=True)
fig.show()

$RBF(l=0.10, $\sigma$=1.00), Simulated Paths, Kernel$

process = GPRBF(length_scale=0.1, sigma=1.0, T=1.0)
fig = process.draw(n=200, N=200, figsize=(12, 7), dpi=150)
fig.show()

$RBF(l=0.10, $\sigma$=1.00), Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal$

process = GPRBF(length_scale=0.1, sigma=1.0, T=1.0)
fig = process.plot(n=200, N=10, figsize=(12, 7), dpi=250)
fig.show()

$RBF(l=0.10, $\sigma$=1.00)$

Total running time of the script: (0 minutes 1.485 seconds)

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