Create a gaussian process from a covariance modelΒΆ

In this basic example we are going to build a gaussian process from its covariance model.

In [28]:
from __future__ import print_function
import openturns as ot
In [29]:
# define a covariance model
defaultDimension = 1
# Amplitude values
amplitude = [1.0]*defaultDimension
# Scale values
scale = [1.0]*defaultDimension
# Second order model with parameters
myModel = ot.ExponentialCauchy(scale, amplitude)
In [30]:
# define a mesh
tmin = 0.0
step = 0.1
n = 11
myTimeGrid = ot.RegularGrid(tmin, step, n)
In [31]:
# create the process
process = ot.GaussianProcess(myModel, myTimeGrid)
print(process)
GaussianProcess(trend=[x0]->[0.0], covariance=AbsoluteExponential(scale=[1], amplitude=[1]))
In [32]:
# draw a sample
sample = process.getSample(6)
sample.drawMarginal(0)
Out[32]:
../../_images/examples_probabilistic_modeling_gaussian_process_covariance_6_0.svg