Kinetic Bayesian Inference
Project description
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 (maxrc@udel.edu)
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
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