BNLearn’s Documentation

Tip

• Guide in detecting causal relationships using Bayesian Structure Learning in Python.

• Guide in designing knowledge-driven models using Bayesian theorem.

• A Comparative Analysis of Libraries to Reveal Hidden Causality in Your Dataset.

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bnlearn is for learning the graphical structure of Bayesian networks in Python! What benefits does bnlearn offer over other bayesian analysis implementations?

• Build on top of the pgmpy library

• Contains the most-wanted bayesian pipelines

• Simple and intuitive

• Focus on structure learning, parameter learning and inference.

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Note

Your ❤️ is important to keep maintaining this package. You can support in various ways, have a look at the sponser page. Report bugs, issues and feature extensions at github page.

pip install bnlearn

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Contents

Quickstart

• Introduction
  • Structure Learning algorithms
  • Score-based algorithms
  • Local discovery algorithms
  • Bayesian network classifiers
  • Constraint-based structure learning algorithms
• Quickstart
  • Installation (short)
  • Quick Examples

Installation

• Install from Pypi (pip)
• Install from github
• Create environment
• Uninstall
• Validate
• Import Error

Structure learning

• Causation
  • Probability theory
  • Structure learning
• Exhaustivesearch
• Hillclimbsearch
• Chow-liu
• Tree-augmented Naive Bayes (TAN)
• NaiveBayes
• Constraint-based

Parameter learning

• Parameter learning
• Maximum Likelihood Estimation
• Bayesian Parameter Estimation
• Examples Parameter learning
  • Example (1)
  • Example (2)
• Conditional Probability Distributions (CPD)

Inference

• Inference
• Inference Algorithms
• Examples Inference
  • Example (1)
  • Example (2)
  • Example (3)

Continuous Data

• Advanced discretizing continous data
• Structure learning
• Parameter learning
• Inference
  • References

Predict

• Predict

Sampling

• Forward Sampling
• Gibbs Sampling

Plot

• Interactive plot
• Static plot
• Comparison of two networks
• Node properties
• Edge properties

Other functionalities

• Independence test
• Directed Acyclic Graphs
  • Overview
  • Building a causal DAG
  • The causal DAG as a generative representation of joint probability
  • Specifying the probability distributions on your own
  • Inference on the causal generative model
• DataFrames
• Import DAG/BIF
• Black and white lists
• Topological sort
• Data Conversions
  • Adjacency matrix
  • Vector
  • adjmat2vec
  • vec2adjmat
  • adjmat2dict
  • dag2adjmat
  • vec2df
• Structure Scores
• Saving and Loading

Examples

• Start with RAW data
• Structure learning
  • Example (1)
  • Example (2)
  • Example (3)
• Parameter learning
• Create a Bayesian Network, learn its parameters from data and perform the inference
• Use Case Titanic
• Use Case Medical domain
  • Description
  • Import data
  • Make inferences when you have data and know-how
  • Determine causalities when you have data
  • Make inference when you have data

Parameters and attributes

• bnlearn.structure_learning
  • fit()
• bnlearn.parameter_learning
  • Description
  • fit()
• bnlearn.inference
  • fit()
• bnlearn.bnlearn
  • adjmat2dict()
  • adjmat2vec()
  • check_model()
  • compare_networks()
  • dag2adjmat()
  • df2onehot()
  • get_edge_properties()
  • get_node_properties()
  • import_DAG()
  • import_example()
  • independence_test()
  • load()
  • make_DAG()
  • plot()
  • predict()
  • print_CPD()
  • query2df()
  • sampling()
  • save()
  • structure_scores()
  • to_bayesiannetwork()
  • to_undirected()
  • topological_sort()
  • vec2adjmat()
  • vec2df()

Documentation

• Sponsor
• Medium Blog
• Github
• Colab Notebook
• Citing
• References
• Related Packages

Indices and tables

• Index

• Module Index

• Search Page

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