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Top 3 Python bayesian-network Projects
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dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
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pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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causalnex
A Python library that helps data scientists to infer causation rather than observing correlation.
I'm a fan of the Do Why library out of Microsoft. Even as a novice in the field of causal modeling it can get you up and running by estimating the causal graph based on your data. https://github.com/py-why/dowhy
Python bayesian-networks related posts
- Acceptable data formats for Predictive Stepwise Logistic Regression
- Do you use any specific framework when it comes to causal inference?
- Causal Explanations Considered Harmful: On the logical fallacy of causal projection
- [Q] What are some of the most useful topics/classes in philosophy for Statistics?
- [R] DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
- How many of you still buy and read textbooks after your degree?
- Use data from tables generated in python console,
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Index
What are some of the best open-source bayesian-network projects in Python? This list will help you:
Project | Stars | |
---|---|---|
1 | dowhy | 6,737 |
2 | pgmpy | 2,617 |
3 | causalnex | 2,144 |
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