Biolith Documentation
Biolith: Bayesian Ecological Modeling in Python
Biolith is a Python package designed for Bayesian ecological modeling and analysis with a focus on occupancy modeling. It has similar goals to Unmarked and spOccupancy, but is written in Python and uses NumPyro and JAX to enable rapid model fitting and iteration.
Features
Hackable: Models are easy to understand and implement, no likelihood derivations needed.
Fast: Models can be fit on GPUs, which is fast.
Familiar: Everything is written in Python, making it easy to integrate into existing pipelines.
Installation
You can install Biolith using pip:
pip install biolith
Usage
Here is a simple example using simulated data to get you started:
from biolith.models import occu, simulate
from biolith.utils import fit
# Simulate dataset
data, true_params = simulate()
# Fit model to simulated data
results = fit(occu, **data)
# Compare estimated occupancy probability to the true mean occupancy
print(f"Mean estimated psi: {results.samples['psi'].mean():.2f}")
print(f"Mean true occupancy: {true_params['z'].mean():.2f}")
Real-world Example
To see a real-world example on camera trap data, see this Jupyter Notebook from the EFI Statistical Methods Seminar Series or 
Documentation
API Documentation and examples are available here.
Development
Run python -m unittest to run unit tests.
Run scripts/format.sh to format the codebase. Execute scripts/check.sh to run
isort and black in check mode along with pylint and pyright.
To install the pre-commit hook for formatting and code linting, run:
./scripts/install_precommit.sh
License
This project is licensed under the MIT License. See the LICENSE file for details.
Contact
For questions or feedback, please open an issue or email haucke@mit.edu.
Acknowledgements
This work was supported by the MIT-IBM Watson AI Lab and Goldman Sachs. This work was supported by the AI and Biodiversity Change (ABC) Global Center, which is funded by the US National Science Foundation under Award No. 2330423 and Natural Sciences and Engineering Research Council of Canada under Award No. 585136. This work draws on research supported in part by the Social Sciences and Humanities Research Council.