Metadata-Version: 2.1
Name: lampe
Version: 0.4.3
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
Description: <p align="center"><img src="https://raw.githubusercontent.com/francois-rozet/lampe/master/sphinx/static/banner.svg" width="100%"></p>
        
        # 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.
        
        ## 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
        ```
        
        ## 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).
        
Keywords: parameter inference bayes posterior amortized likelihood ratio mcmc torch
Platform: UNKNOWN
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
