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 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 from disk, diagnose predictions and display results graphically.
Installation
The lampe package is available on PyPI, 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/probabilists/lampe
Documentation
The documentation is made with Sphinx and Furo and is hosted at lampe.readthedocs.io.
Contributing
If you have a question, an issue or would like to contribute, please read our contributing guidelines.
1 maintainer
Metadata
1 maintainer
Release files for lampe 0.9.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lampe-0.9.0.tar.gz | 28.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lampe-0.9.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 59.7 kB
Release files / lampe-0.9.0.tar.gz
| Download URL | lampe-0.9.0.tar.gz |
|---|---|
| Size | 28.1 kB |
| Tags | Source |
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No |
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twine/5.1.1 CPython/3.9.18
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Release files / lampe-0.9.0-py3-none-any.whl
| Download URL | lampe-0.9.0-py3-none-any.whl |
|---|---|
| Size | 31.6 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
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twine/5.1.1 CPython/3.9.18
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