PyBNesian
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davenza
PyBNesian is a Python package that implements Bayesian networks.
Hello! If someone ever considers to return to this package, please, keep in mind [that apache change:](https://github.com/apache/arrow/pull/11792) - `BitUtil` namespace has been renamed to `bit_utls` So the code won't be...
Hello! I wanted to try your library, but unfortunately I didn’t succeed, there’s really not enough documentation. For example, it is not entirely clear how to use hybrid semiparametric networks,...
For those interested in visualizing Graph or GaussianNetwork objects, I have developed this code: ``` !pip install networkx import networkx as nx def visualizeGraph(bnGraph): G = nx.DiGraph() G.add_edges_from(bnGraph.arcs(), weight=1) return...
**Issue: Printing nodes again when arcs should be shown**  Code must be: ``print("Arcs: " + str(gbn.arcs()))`` Instead of the highlighted code
### Brief Context Hello, I find your library an excellent tool for Bayesian network research. I'm currently trying to work with hybrid Bayesian networks (Discrete and Gaussian CPDs). In your...
Can someone show an example how to use this library to conduct conditional independence tests with mixed data? Suppose I have the following data. I am confused on how to...
On cross-compile environments it is sometimes advantageous to use environment variables while building. This gives the project more flexibility and better compatibility with different build systems.
Dear author, Thanks for sharing this. I'm wondering if this codebase support structure and parameter learning for hybrid(contains discrete and continuous variables) DBN?
- Python code is now formatted with `black` and `isort`, and has been refactored according to `PEP 8` style guides. - Python code partially commented with `google` docstring format. -...