Metadata-Version: 2.1
Name: lampe
Version: 0.5.4
Summary: Likelihood-free AMortized Posterior Estimation with PyTorch
Home-page: https://github.com/francois-rozet/lampe
Author: François Rozet
Author-email: francois.rozet@outlook.com
License: MIT license
Project-URL: Documentation, https://github.com/francois-rozet/lampe
Project-URL: Source, https://github.com/francois-rozet/lampe
Project-URL: Tracker, https://github.com/francois-rozet/lampe/issues
Keywords: parameter inference bayes posterior amortized likelihood ratio mcmc torch
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: docs
Provides-Extra: test
License-File: LICENSE

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# LAMPE

`lampe` is a simulation-based inference (SBI) package that focuses on amortized estimation of posterior distributions, without relying on explicit likelihood functions; hence the name *Likelihood-free AMortized Posterior Estimation* (LAMPE). The package provides [PyTorch](https://pytorch.org) implementations of modern amortized simulation-based inference algorithms like neural ratio estimation (NRE), neural posterior estimation (NPE) and more. Similar to PyTorch, the philosophy of LAMPE is to avoid obfuscation and expose all components, from network architecture to optimizer, to the user such that they are free to modify or replace anything they like.

As part of the inference pipeline, LAMPE provides components to efficiently [store and load data](lampe/data.py) from disk, [diagnose predictions](lampe/diagnostics.py) and [display results](lampe/plots.py) graphically. The package also implements [normalizing flows](lampe/nn/flows.py) from scratch in a way that is both easy to understand and extend.

## Installation

The `lampe` package is available on [PyPI](https://pypi.org/project/lampe), which means it is installable via `pip`.

```
pip install lampe
```

Alternatively, if you need the latest features, you can install it from the repository.

```
pip install git+https://github.com/francois-rozet/lampe
```

## Contributing

If you have a question, an issue or would like to contribute, please read our [contributing guidelines](CONTRIBUTING.md).

## Documentation

The documentation is made with [Sphinx](https://www.sphinx-doc.org) and [Furo](https://github.com/pradyunsg/furo) and is hosted at [francois-rozet.github.io/lampe](https://francois-rozet.github.io/lampe).
