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Implemented tensorflow version of the Bullseye method for prior approximation.

Project description

# Bullseye!

"Bullseye!" is a new algorithm for computing the Gaussian Variational Approximation of a target distribution. Its strong point lies in the fact that it can easily be parallelized and distributed.

## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

### Installing

Bullseye! is now available as a [PyPI package](

pip install bullseye_method

or clone the repository :

git clone

or [download and extract the zip]( into your project folder.

## Running the tests

To see if everything is working properly, you can already run the algorithm on a multilogit model with artificially generated data.

from Bullseye.Tests import simple_test

## Example

import Bullseye
from Bullseye import generate_multilogit

theta_0, x_array, y_array = generate_multilogit(d = 10, n = 10**3, k = 5)

bull = Bullseye.Graph()
bull.init_with(mu_0 = 0, cov_0 = 1)
bull.set_options(local_std_trick = True, s=5)

## Authors

* **Quentin Lévêque** [Whenti](

See also the list of [contributors]( who participated in this project. Hopefully, there will be more.

## License

This project licensed under the GPL3 License - see the [LICENSE.txt](LICENSE.txt) file for details.

Project details

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