# Bayesian Optimal Experiment Design
[](https://pypi.org/project/bayesdesign/) [](https://github.com/dkirkby/bayesdesign/releases) [](https://github.com/dkirkby/bayesdesign/actions) [](https://github.com/dkirkby/bayesdesign/blob/main/LICENSE)
Use this package to calculate expected information gain for Bayesian optimal experiment design. For an introduction to this topic, see this [interactive notebook](https://observablehq.com/@dkirkby/boed). To perform a similar calculation with this package, use:
```python
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](https://github.com/dkirkby/bayesdesign/) to learn more about using this package.
## Installation
Install the [latest released version](https://github.com/dkirkby/bayesdesign/releases/latest) from [pypi](https://pypi.org/project/bayesdesign/) 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](CHANGELOG.md).
## Upgrade
To upgrade your pip-installed package to the [latest released version](https://github.com/dkirkby/bayesdesign/releases/latest) use:
```
pip install bayesdesign --upgrade
```
## Contributing
If you have feedback or would like to contribute to this package, please see our [contributor's guide](CONTRIBUTING.md).
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