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Gamma Process#
Simulate and visualise paths
# Author: Dialid Santiago <d.santiago@outlook.com>
# License: MIT
# Description: Simulate and visualise a Gamma Process
from aleatory.processes import GammaProcess
from aleatory.styles import qp_style
qp_style() # Use quant-pastel-style
p = GammaProcess()
fig = p.draw(n=200, N=200, figsize=(12, 7), colormap="winter")
fig.show()
![Gamma Process X($\mu$=1.0, $\nu$=1.0), Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal](../_images/sphx_glr_plot_gamma_001.png)
p = GammaProcess(mu=2.0, nu=1.5, T=20.0)
fig = p.draw(n=200, N=200, figsize=(12, 7), colormap="YlGn")
fig.show()
![Gamma Process X($\mu$=2.0, $\nu$=1.5), Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal](../_images/sphx_glr_plot_gamma_002.png)
p = GammaProcess(mu=1.5, nu=0.5, T=10)
fig = p.plot(n=100, N=10, figsize=(12, 7))
fig.show()
Total running time of the script: (0 minutes 2.725 seconds)