Welcome to Gaussian Toolbox’s documentation!

Install

Just use the following commands

pip install git+https://github.com/christiando/gaussian-toolbox

Getting started

Then you can already get started:

from matplotlib import pyplot as plt
from jax import numpy as jnp
from gaussian_toolbox as gt

R, D = 10, 1
mu = jnp.zeros((R, D))
Sigma = jnp.ones((R, D, D))
Sigma = Sigma.at[:,0,0].set(jnp.linspace(.1,1,R))


p_X = gt.pdf.GaussianPDF(Sigma=Sigma, mu=mu)

x = jnp.linspace(-5,5,1000)[:,None]

plt.plot(x[:,0], p_X(x).T)
plt.xlabel('X')
plt.ylabel('p(X)')
plt.show()

For more details find the tutorials below.

Introduction:

• Simple Gaussian objects
• Conditional densities and affine transformations
• Example: Bayesian linear regression
• Kalman Filter
• Approximate conditionals

API

• gaussian_toolbox package
  • Subpackages
  • Submodules
  • gaussian_toolbox.approximate_conditional module
  • gaussian_toolbox.conditional module
  • gaussian_toolbox.factor module
  • gaussian_toolbox.measure module
  • gaussian_toolbox.pdf module
  • Module contents