aleatory.processes.GPLinear#

class aleatory.processes.GPLinear(sigma=1.0, c=0.0, sigma_b=0.0, T=1.0, rng=None)[source]#

Gaussian Process with Linear Kernel#

A centered Gaussian Process with covariance function given by

\[K(t, s) = \sigma^2 ts, \ \ \ \ t, s \in [0,T]\]
__init__(sigma=1.0, c=0.0, sigma_b=0.0, T=1.0, rng=None)[source]#

Methods

__init__([sigma, c, sigma_b, T, rng])

covariance_function(times)

draw(n, N[, T, marginal, envelope, type, title])

Simulates and plots paths/trajectories from the instanced stochastic process.

estimate_covariances([times])

estimate_expectations()

estimate_quantiles(q)

estimate_stds()

estimate_variances()

get_marginal(time)

make_widget([cmap, matrix_shape])

marginal_expectation([times])

marginal_stds([times])

marginal_variance([times])

plot(n, N[, T, title, suptitle])

Simulates and plots paths/trajectories from the instanced stochastic process.

plot_covariance([times, colormap, ...])

plot_kernel([times, colormap, matrix_shape, ...])

plot_kernel3d([times, title])

plot_mean_function([T, n])

plot_mean_variance([times])

plot_paths_and_kernel(n, N[, T, cmap, ...])

Plots the paths of the process and the covariance kernel.

process_covariance([times])

process_expectation()

process_stds()

process_variance()

sample(n[, T])

sample_at(times)

simulate(n, N[, T])

Simulate paths/trajectories from the instanced stochastic process.

variance_function(times)

Attributes

T

End time of the process.

rng