Bayesian Optimal Experiment Design

Use this package to calculate expected information gain for Bayesian optimal experiment design. For an introduction to this topic, see this interactive notebook. To perform a similar calculation with this package, use:

from bed.grid import Grid, GridStack
from bed.design import ExperimentDesigner

designs = Grid(t_obs=np.linspace(0, 5, 51))
features = Grid(y_obs=np.linspace(-1.25, 1.25, 100))
params = Grid(amplitude=1, frequency=np.linspace(0.2, 2.0, 181), offset=0)

sigma_y=0.1
with GridStack(features, designs, params):
    y_mean = params.amplitude * np.sin(params.frequency * (designs.t_obs - params.offset))
    y_diff = features.y_obs - y_mean
    likelihood = np.exp(-0.5 * (y_diff / sigma_y) ** 2)
    features.normalize(likelihood)

designer = ExperimentDesigner(params, features, designs, likelihood)

prior = np.ones(params.shape)
params.normalize(prior);

designer.calculateEIG(prior)

plt.plot(designs.t_obs, designer.EIG)

Browse the examples folder to learn more about using this package.

To run the examples in google colab, select GitHub and enter https://github.com/dkirkby/bayesdesign.

Installation

Install the latest released version from pypi using:

pip install bayesdesign

The only required dependency is numpy. The optional plot module also requires matplotlib.

The changes with each version are documented here.

Upgrade

To upgrade your pip-installed package to the latest released version use:

pip install bayesdesign --upgrade

Contributing

If you have feedback or would like to contribute to this package, please see our contributor's guide.