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## `sbi`: Simulation-Based Inference
[Getting Started](https://sbi-dev.github.io/sbi/latest/tutorials/00_getting_started/) |
[Documentation](https://sbi-dev.github.io/sbi/)
`sbi` is a Python package for simulation-based inference, designed to meet the needs of
both researchers and practitioners. Whether you need fine-grained control or an
easy-to-use interface, `sbi` has you covered.
With `sbi`, you can perform parameter inference using Bayesian inference: Given a
simulator that models a real-world process, SBI estimates the full posterior
distribution over the simulator’s parameters based on observed data. This distribution
indicates the most likely parameter values while additionally quantifying uncertainty
and revealing potential interactions between parameters.
### Key Features of `sbi`
`sbi` offers a blend of flexibility and ease of use:
- **Low-Level Interfaces**: For those who require maximum control over the inference
process, `sbi` provides low-level interfaces that allow you to fine-tune many aspects
of your workflow.
- **High-Level Interfaces**: If you prefer simplicity and efficiency, `sbi` also offers
high-level interfaces that enable quick and easy implementation of complex inference
tasks.
In addition, `sbi` supports a wide range of state-of-the-art inference algorithms (see
below for a list of implemented methods):
- **Amortized Methods**: These methods enable the reuse of posterior estimators across
multiple observations without the need to retrain.
- **Sequential Methods**: These methods focus on individual observations, optimizing the
number of simulations required.
Beyond inference, `sbi` also provides:
- **Validation Tools**: Built-in methods to validate and verify the accuracy of your
inferred posteriors.
- **Plotting and Analysis Tools**: Comprehensive functions for visualizing and analyzing
results, helping you interpret the posterior distributions with ease.
Getting started with `sbi` is straightforward, requiring only a few lines of code:
```python
from sbi.inference import NPE
# Given: parameters theta and corresponding simulations x
inference = NPE(prior=prior)
inference.append_simulations(theta, x).train()
posterior = inference.build_posterior()
```
### Installation
`sbi` requires Python 3.9 or higher. While a GPU isn't necessary, it can improve
performance in some cases. We recommend using a virtual environment with
[`conda`](https://docs.conda.io/en/latest/miniconda.html) for an easy setup.
To install `sbi`, follow these steps:
1. **Create a Conda Environment** (if using Conda):
```bash
conda create -n sbi_env python=3.9 && conda activate sbi_env
```
2. **Install `sbi`**: Independent of whether you are using `conda` or not, `sbi` can be
installed using `pip`:
```commandline
pip install sbi
```
3. **Test the installation**:
Open a Python prompt and run
```python
from sbi.examples.minimal import simple
posterior = simple()
print(posterior)
```
## Tutorials
If you're new to `sbi`, we recommend starting with our [Getting
Started](https://sbi-dev.github.io/sbi/latest/tutorials/00_getting_started/) tutorial.
You can also access and run these tutorials directly in your browser by opening
[Codespace](https://docs.github.com/en/codespaces/overview). To do so, click the green
“Code” button on the GitHub repository and select “Open with Codespaces.” This provides
a fully functional environment where you can explore `sbi` through Jupyter notebooks.
## Inference Algorithms
The following inference algorithms are currently available. You can find instructions on
how to run each of these methods
[here](https://sbi-dev.github.io/sbi/latest/tutorials/16_implemented_methods/).
### Neural Posterior Estimation: amortized (NPE) and sequential (SNPE)
* [`(S)NPE_A`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.npe.npe_a.NPE_A)
(including amortized single-round `NPE`) from Papamakarios G and Murray I [_Fast
ε-free Inference of Simulation Models with Bayesian Conditional Density
Estimation_](https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html)
(NeurIPS 2016).
* [`(S)NPE_C`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.npe.npe_c.NPE_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).
* `TSNPE` from Deistler M, Goncalves P, and Macke J [_Truncated proposals for scalable
and hassle-free simulation-based inference_](https://arxiv.org/abs/2210.04815)
(NeurIPS 2022).
* [`FMPE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.fmpe.fmpe.FMPE)
from Wildberger, J., Dax, M., Buchholz, S., Green, S., Macke, J. H., & Schölkopf, B.
[_Flow matching for scalable simulation-based
inference_](https://proceedings.neurips.cc/paper_files/paper/2023/hash/3663ae53ec078860bb0b9c6606e092a0-Abstract-Conference.html).
(NeurIPS 2023).
* [`NPSE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.npse.npse.NPSE) from
Geffner, T., Papamakarios, G., & Mnih, A. [_Compositional score modeling for
simulation-based inference_](https://proceedings.mlr.press/v202/geffner23a.html).
(ICML 2023)
### Neural Likelihood Estimation: amortized (NLE) and sequential (SNLE)
* [`(S)NLE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nle.nle_a.NLE_A)
or just `SNL` from Papamakarios G, Sterrat DC and Murray I [_Sequential Neural
Likelihood_](https://arxiv.org/abs/1805.07226) (AISTATS 2019).
### Neural Ratio Estimation: amortized (NRE) and sequential (SNRE)
* [`(S)NRE_A`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.nre_a.NRE_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).
* [`(S)NRE_B`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.nre_b.NRE_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).
* [`(S)NRE_C`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.nre_c.NRE_C)
or `NRE-C` from Miller BK, Weniger C, Forré P. [_Contrastive Neural Ratio
Estimation_](https://arxiv.org/abs/2210.06170) (NeurIPS 2022).
* [`BNRE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.bnre.BNRE) from
Delaunoy A, Hermans J, Rozet F, Wehenkel A, and Louppe G. [_Towards Reliable
Simulation-Based Inference with Balanced Neural Ratio
Estimation_](https://arxiv.org/abs/2208.13624) (NeurIPS 2022).
### Neural Variational Inference, amortized (NVI) and sequential (SNVI)
* [`SNVI`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.posteriors.vi_posterior)
from Glöckler M, Deistler M, Macke J, [_Variational methods for simulation-based
inference_](https://openreview.net/forum?id=kZ0UYdhqkNY) (ICLR 2022).
### Mixed Neural Likelihood Estimation (MNLE)
* [`MNLE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nle.mnle.MNLE) from
Boelts J, Lueckmann JM, Gao R, Macke J, [_Flexible and efficient simulation-based
inference for models of decision-making_](https://elifesciences.org/articles/77220)
(eLife 2022).
## Feedback and Contributions
We welcome any feedback on how `sbi` is working for your inference problems (see
[Discussions](https://github.com/sbi-dev/sbi/discussions)) and are happy to receive bug
reports, pull requests, and other feedback (see
[contribute](https://sbi-dev.github.io/sbi/latest/contribute/)). We wish to maintain a positive
community; please read our [Code of Conduct](CODE_OF_CONDUCT.md).
## Acknowledgments
`sbi` is the successor (using PyTorch) of the
[`delfi`](https://github.com/mackelab/delfi) package. It started as a fork of Conor M.
Durkan's `lfi`. `sbi` runs as a community project. See also
[credits](https://github.com/sbi-dev/sbi/blob/master/docs/docs/credits.md).
## Support
`sbi` has been supported by the German Federal Ministry of Education and Research (BMBF)
through project ADIMEM (FKZ 01IS18052 A-D), project SiMaLeSAM (FKZ 01IS21055A) and the
Tübingen AI Center (FKZ 01IS18039A). Since 2024, `sbi` is supported by the appliedAI
Institute for Europe.
## License
[Apache License Version 2.0 (Apache-2.0)](https://www.apache.org/licenses/LICENSE-2.0)
## Citation
If you use `sbi` consider citing the [sbi software
paper](https://doi.org/10.21105/joss.02505), in addition to the original research
articles describing the specific sbi-algorithm(s) you are using.
```latex
@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}
}
```
The above citation refers to the original version of the `sbi` project and has a
persistent DOI. Additionally, new releases of `sbi` are citable via
[Zenodo](https://zenodo.org/record/3993098), where we create a new DOI for every
release.
Raw data
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"description": "[](https://badge.fury.io/py/sbi)\n[](https://github.com/sbi-dev/sbi/blob/master/CONTRIBUTING.md)\n[](https://github.com/sbi-dev/sbi/actions)\n[](https://codecov.io/gh/sbi-dev/sbi)\n[](https://github.com/sbi-dev/sbi/blob/master/LICENSE.txt)\n[](https://doi.org/10.21105/joss.02505)\n\n## `sbi`: Simulation-Based Inference\n\n[Getting Started](https://sbi-dev.github.io/sbi/latest/tutorials/00_getting_started/) |\n[Documentation](https://sbi-dev.github.io/sbi/)\n\n`sbi` is a Python package for simulation-based inference, designed to meet the needs of\nboth researchers and practitioners. Whether you need fine-grained control or an\neasy-to-use interface, `sbi` has you covered.\n\nWith `sbi`, you can perform parameter inference using Bayesian inference: Given a\nsimulator that models a real-world process, SBI estimates the full posterior\ndistribution over the simulator\u2019s parameters based on observed data. This distribution\nindicates the most likely parameter values while additionally quantifying uncertainty\nand revealing potential interactions between parameters.\n\n### Key Features of `sbi`\n\n`sbi` offers a blend of flexibility and ease of use:\n\n- **Low-Level Interfaces**: For those who require maximum control over the inference\n process, `sbi` provides low-level interfaces that allow you to fine-tune many aspects\n of your workflow.\n- **High-Level Interfaces**: If you prefer simplicity and efficiency, `sbi` also offers\n high-level interfaces that enable quick and easy implementation of complex inference\n tasks.\n\nIn addition, `sbi` supports a wide range of state-of-the-art inference algorithms (see\nbelow for a list of implemented methods):\n\n- **Amortized Methods**: These methods enable the reuse of posterior estimators across\n multiple observations without the need to retrain.\n- **Sequential Methods**: These methods focus on individual observations, optimizing the\n number of simulations required.\n\nBeyond inference, `sbi` also provides:\n\n- **Validation Tools**: Built-in methods to validate and verify the accuracy of your\n inferred posteriors.\n- **Plotting and Analysis Tools**: Comprehensive functions for visualizing and analyzing\n results, helping you interpret the posterior distributions with ease.\n\nGetting started with `sbi` is straightforward, requiring only a few lines of code:\n\n```python\nfrom sbi.inference import NPE\n# Given: parameters theta and corresponding simulations x\ninference = NPE(prior=prior)\ninference.append_simulations(theta, x).train()\nposterior = inference.build_posterior()\n```\n\n### Installation\n\n`sbi` requires Python 3.9 or higher. While a GPU isn't necessary, it can improve\nperformance in some cases. We recommend using a virtual environment with\n[`conda`](https://docs.conda.io/en/latest/miniconda.html) for an easy setup.\n\nTo install `sbi`, follow these steps:\n\n1. **Create a Conda Environment** (if using Conda):\n\n ```bash\n conda create -n sbi_env python=3.9 && conda activate sbi_env\n ```\n\n2. **Install `sbi`**: Independent of whether you are using `conda` or not, `sbi` can be\n installed using `pip`:\n\n ```commandline\n pip install sbi\n ```\n\n3. **Test the installation**:\nOpen a Python prompt and run\n\n```python\nfrom sbi.examples.minimal import simple\nposterior = simple()\nprint(posterior)\n```\n\n## Tutorials\n\nIf you're new to `sbi`, we recommend starting with our [Getting\nStarted](https://sbi-dev.github.io/sbi/latest/tutorials/00_getting_started/) tutorial.\n\nYou can also access and run these tutorials directly in your browser by opening\n[Codespace](https://docs.github.com/en/codespaces/overview). To do so, click the green\n\u201cCode\u201d button on the GitHub repository and select \u201cOpen with Codespaces.\u201d This provides\na fully functional environment where you can explore `sbi` through Jupyter notebooks.\n\n## Inference Algorithms\n\nThe following inference algorithms are currently available. You can find instructions on\nhow to run each of these methods\n[here](https://sbi-dev.github.io/sbi/latest/tutorials/16_implemented_methods/).\n\n### Neural Posterior Estimation: amortized (NPE) and sequential (SNPE)\n\n* [`(S)NPE_A`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.npe.npe_a.NPE_A)\n (including amortized single-round `NPE`) from Papamakarios G and Murray I [_Fast\n \u03b5-free Inference of Simulation Models with Bayesian Conditional Density\n Estimation_](https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html)\n (NeurIPS 2016).\n\n* [`(S)NPE_C`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.npe.npe_c.NPE_C)\n or `APT` from Greenberg D, Nonnenmacher M, and Macke J [_Automatic Posterior\n Transformation for likelihood-free inference_](https://arxiv.org/abs/1905.07488) (ICML\n 2019).\n\n* `TSNPE` from Deistler M, Goncalves P, and Macke J [_Truncated proposals for scalable\n and hassle-free simulation-based inference_](https://arxiv.org/abs/2210.04815)\n (NeurIPS 2022).\n\n* [`FMPE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.fmpe.fmpe.FMPE)\n from Wildberger, J., Dax, M., Buchholz, S., Green, S., Macke, J. H., & Sch\u00f6lkopf, B.\n [_Flow matching for scalable simulation-based\n inference_](https://proceedings.neurips.cc/paper_files/paper/2023/hash/3663ae53ec078860bb0b9c6606e092a0-Abstract-Conference.html).\n (NeurIPS 2023).\n\n* [`NPSE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.npse.npse.NPSE) from\n Geffner, T., Papamakarios, G., & Mnih, A. [_Compositional score modeling for\n simulation-based inference_](https://proceedings.mlr.press/v202/geffner23a.html).\n (ICML 2023)\n\n### Neural Likelihood Estimation: amortized (NLE) and sequential (SNLE)\n\n* [`(S)NLE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nle.nle_a.NLE_A)\n or just `SNL` from Papamakarios G, Sterrat DC and Murray I [_Sequential Neural\n Likelihood_](https://arxiv.org/abs/1805.07226) (AISTATS 2019).\n\n### Neural Ratio Estimation: amortized (NRE) and sequential (SNRE)\n\n* [`(S)NRE_A`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.nre_a.NRE_A)\n or `AALR` from Hermans J, Begy V, and Louppe G. [_Likelihood-free Inference with\n Amortized Approximate Likelihood Ratios_](https://arxiv.org/abs/1903.04057) (ICML\n 2020).\n\n* [`(S)NRE_B`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.nre_b.NRE_B)\n or `SRE` from Durkan C, Murray I, and Papamakarios G. [_On Contrastive Learning for\n Likelihood-free Inference_](https://arxiv.org/abs/2002.03712) (ICML 2020).\n\n* [`(S)NRE_C`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.nre_c.NRE_C)\n or `NRE-C` from Miller BK, Weniger C, Forr\u00e9 P. [_Contrastive Neural Ratio\n Estimation_](https://arxiv.org/abs/2210.06170) (NeurIPS 2022).\n\n* [`BNRE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nre.bnre.BNRE) from\n Delaunoy A, Hermans J, Rozet F, Wehenkel A, and Louppe G. [_Towards Reliable\n Simulation-Based Inference with Balanced Neural Ratio\n Estimation_](https://arxiv.org/abs/2208.13624) (NeurIPS 2022).\n\n\n### Neural Variational Inference, amortized (NVI) and sequential (SNVI)\n\n* [`SNVI`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.posteriors.vi_posterior)\n from Gl\u00f6ckler M, Deistler M, Macke J, [_Variational methods for simulation-based\n inference_](https://openreview.net/forum?id=kZ0UYdhqkNY) (ICLR 2022).\n\n### Mixed Neural Likelihood Estimation (MNLE)\n\n* [`MNLE`](https://sbi-dev.github.io/sbi/latest/reference/#sbi.inference.trainers.nle.mnle.MNLE) from\n Boelts J, Lueckmann JM, Gao R, Macke J, [_Flexible and efficient simulation-based\n inference for models of decision-making_](https://elifesciences.org/articles/77220)\n (eLife 2022).\n\n## Feedback and Contributions\n\nWe welcome any feedback on how `sbi` is working for your inference problems (see\n[Discussions](https://github.com/sbi-dev/sbi/discussions)) and are happy to receive bug\nreports, pull requests, and other feedback (see\n[contribute](https://sbi-dev.github.io/sbi/latest/contribute/)). We wish to maintain a positive\ncommunity; please read our [Code of Conduct](CODE_OF_CONDUCT.md).\n\n## Acknowledgments\n\n`sbi` is the successor (using PyTorch) of the\n[`delfi`](https://github.com/mackelab/delfi) package. It started as a fork of Conor M.\nDurkan's `lfi`. `sbi` runs as a community project. See also\n[credits](https://github.com/sbi-dev/sbi/blob/master/docs/docs/credits.md).\n\n## Support\n\n`sbi` has been supported by the German Federal Ministry of Education and Research (BMBF)\nthrough project ADIMEM (FKZ 01IS18052 A-D), project SiMaLeSAM (FKZ 01IS21055A) and the\nT\u00fcbingen AI Center (FKZ 01IS18039A). Since 2024, `sbi` is supported by the appliedAI\nInstitute for Europe.\n\n## License\n\n[Apache License Version 2.0 (Apache-2.0)](https://www.apache.org/licenses/LICENSE-2.0)\n\n## Citation\n\nIf you use `sbi` consider citing the [sbi software\npaper](https://doi.org/10.21105/joss.02505), in addition to the original research\narticles describing the specific sbi-algorithm(s) you are using.\n\n```latex\n@article{tejero-cantero2020sbi,\n doi = {10.21105/joss.02505},\n url = {https://doi.org/10.21105/joss.02505},\n year = {2020},\n publisher = {The Open Journal},\n volume = {5},\n number = {52},\n pages = {2505},\n author = {Alvaro Tejero-Cantero and Jan Boelts and Michael Deistler and Jan-Matthis Lueckmann and Conor Durkan and Pedro J. Gon\u00e7alves and David S. Greenberg and Jakob H. Macke},\n title = {sbi: A toolkit for simulation-based inference},\n journal = {Journal of Open Source Software}\n}\n```\n\nThe above citation refers to the original version of the `sbi` project and has a\npersistent DOI. Additionally, new releases of `sbi` are citable via\n[Zenodo](https://zenodo.org/record/3993098), where we create a new DOI for every\nrelease.\n",
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