ckbit 1.0.0

Kinetic Bayesian Inference

The Chemical Kinetic Bayesian Inference Toolbox (CKBIT) is a Python library for applying Bayesian inference to kinetic rate parameters developed by the Vlachos Research Group at the University of Delaware.

Documentation

Documentation can be found at this webiste: https://vlachosgroup.github.io/ckbit/

Examples

There are examples of the code in the Github examples folder. The examples are provided in both Python scripts and in Jupyter notebooks. Ensure the accompanying Excel files are used as templates for data entry.

Developers

Max Cohen ([e-mail])

Dependencies

• PyStan2: Interfaces with Stan for optimized Bayesian inference computation - archieved repository

• Datetime: Measures computational runtime

• NumPy: Provides efficient array manipulation

• Pickle: Creates and stores portable, serialized representations of Python objects for repeat model usage

• Hashlib: Interfaces to hash functions for naming stored models

• Matplotlib: Visualizes data outputs

• Pandas: Interfaces with Excel for facile data processing of inputs

• ArviZ: Provides specialized visualization of inference outputs

• Vunits: Converts common physical units

• Tabulate: Generates tabulated displays of inference outputs

Getting Started

See the installation html file in the docs folder for detailed instructions.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing and Questions

If you have a suggestion, find a bug, or have a question, please post to our Issues page on the Github.

Funding

We acknowledge support by the RAPID manufacturing institute, supported by the Department of Energy (DOE) Advanced Manufacturing Office (AMO), award number DE-EE0007888-9.5. RAPID projects at the University of Delaware are also made possible in part by funding provided by the State of Delaware. The Delaware Energy Institute gratefully acknowledges the support and partnership of the State of Delaware in furthering the essential scientific research being conducted through the RAPID projects.

Special Thanks

• Dr. Jonathan Lym

• Dr. Jeffrey Frey