Metadata-Version: 2.1
Name: sbi
Version: 0.14.2
Summary: Simulation-based inference.
Home-page: https://github.com/mackelab/sbi
Author: Álvaro Tejero-Cantero, Jakob H. Macke, Jan-Matthis Lückmann, Conor M. Durkan, Michael Deistler, Jan Bölts
Author-email: sbi@mackelab.org
License: AGPLv3
Description: 
        [![PyPI version](https://badge.fury.io/py/sbi.svg)](https://badge.fury.io/py/sbi)
        [![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/mackelab/sbi/blob/master/CONTRIBUTING.md)
        [![Tests](https://github.com/mackelab/sbi/workflows/Tests/badge.svg?branch=main)](https://github.com/mackelab/sbi/actions)
        [![codecov](https://codecov.io/gh/mackelab/sbi/branch/main/graph/badge.svg)](https://codecov.io/gh/mackelab/sbi)
        [![GitHub license](https://img.shields.io/github/license/mackelab/sbi)](https://github.com/mackelab/sbi/blob/master/LICENSE.txt)
        [![DOI](https://joss.theoj.org/papers/10.21105/joss.02505/status.svg)](https://doi.org/10.21105/joss.02505)
        
        ## sbi: simulation-based inference
        [Getting Started](https://www.mackelab.org/sbi/tutorial/00_getting_started/) | [Documentation](https://www.mackelab.org/sbi/)
        
        `sbi` is a PyTorch package for simulation-based inference. Simulation-based inference is  
        the process of finding parameters of a simulator from observations.
        
        `sbi` takes a Bayesian approach and returns a full posterior distribution
        over the parameters, conditional on the observations. This posterior can be amortized (i.e.
        useful for any observation) or focused (i.e. tailored to a particular observation), with different
        computational trade-offs.
        
        `sbi` offers a simple interface for one-line posterior inference.
        
        ```python
        from sbi.inference import infer
        # import your simulator, define your prior over the parameters
        parameter_posterior = infer(simulator, prior, method='SNPE', num_simulations=100)
        ```
        See below for the available methods of inference, `SNPE`, `SNRE` and `SNLE`.
        
        
        ## Installation
        
        `sbi` requires Python 3.6 or higher. We recommend to use a [`conda`](https://docs.conda.io/en/latest/miniconda.html) virtual
        environment ([Miniconda installation instructions](https://docs.conda.io/en/latest/miniconda.html])). If `conda` is installed on the system, an environment for
        installing `sbi` can be created as follows:
        ```commandline
        # Create an environment for sbi (indicate Python 3.6 or higher); activate it
        $ conda create -n sbi_env python=3.7 && conda activate sbi_env
        ```
        
        Independent of whether you are using `conda` or not, `sbi` can be installed using `pip`:
        ```commandline
        $ pip install sbi
        ```
        
        To test the installation, drop into a python prompt and run
        ```python
        from sbi.examples.minimal import simple
        posterior = simple()
        print(posterior)
        ```
        
        ## Inference Algorithms
        
        The following algorithms are currently available:
        
        #### Sequential Neural Posterior Estimation (SNPE)
        
        * [`SNPE_C`](https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_c.SNPE_C) or `APT` from Greenberg D, Nonnenmacher M, and Macke J [_Automatic
          Posterior Transformation for likelihood-free
          inference_](https://arxiv.org/abs/1905.07488) (ICML 2019).
        
        
        #### Sequential Neural Likelihood Estimation (SNLE)
        * [`SNLE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snle.snle_a.SNLE_A) or just `SNL` from Papamakarios G, Sterrat DC and Murray I [_Sequential
          Neural Likelihood_](https://arxiv.org/abs/1805.07226) (AISTATS 2019).
        
        
        #### Sequential Neural Ratio Estimation (SNRE)
        
        * [`SNRE_A`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_a.SNRE_A) or `AALR` from Hermans J, Begy V, and Louppe G. [_Likelihood-free Inference with Amortized Approximate Likelihood Ratios_](https://arxiv.org/abs/1903.04057) (ICML 2020).
        
        * [`SNRE_B`](https://www.mackelab.org/sbi/reference/#sbi.inference.snre.snre_b.SNRE_B) or `SRE` from Durkan C, Murray I, and Papamakarios G. [_On Contrastive Learning for Likelihood-free Inference_](https://arxiv.org/abs/2002.03712) (ICML 2020).
        
        
        ## Feedback and Contributions
        
        We would like to hear how `sbi` is working for your inference problems as well as receive bug reports, pull requests and other feedback (see
        [contribute](http://www.mackelab.org/sbi/contribute/)).
        
        
        ## Acknowledgements
        
        `sbi` is the successor (using PyTorch) of the
        [`delfi`](https://github.com/mackelab/delfi) package. It was started as a fork of Conor
        M. Durkan's `lfi`. `sbi` runs as a community project; development is coordinated at the
        [mackelab](https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning/research/research/cluster-research-groups/professorships/machine-learning-in-science/). See also [credits](https://github.com/mackelab/sbi/blob/master/docs/docs/credits.md).
        
        
        ## Support
        
        `sbi` has been developed in the context of the [ADIMEM
        grant](https://fit.uni-tuebingen.de/Activity/Details?id=6097), project A. ADIMEM is a
        BMBF grant awarded to groups at the Technical University of Munich, University of
        Tübingen and Research Center caesar of the Max Planck Gesellschaft.
        
        
        ## License
        
        [Affero General Public License v3 (AGPLv3)](https://www.gnu.org/licenses/)
        
        
        ## Citation
        If you use `sbi` consider citing the [corresponding paper](https://doi.org/10.21105/joss.02505):
        ```
        @article{tejero-cantero2020sbi,
          doi = {10.21105/joss.02505},
          url = {https://doi.org/10.21105/joss.02505},
          year = {2020},
          publisher = {The Open Journal},
          volume = {5},
          number = {52},
          pages = {2505},
          author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gonçalves and David S. Greenberg and Jakob H. Macke},
          title = {sbi: A toolkit for simulation-based inference},
          journal = {Journal of Open Source Software}
        }
        ```
Keywords: bayesian parameter inference system_identification simulator PyTorch
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: dev
