bayesdesign 0.2.0

Package for Bayesian optimal experimental design

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)

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

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

This package was generated from this template so refer there for details on how to work with VS code, set python testing versions, etc.