BNLearn’s Documentation

Tip

Medium blog: Step-by-step guide for structure learning

Tip

Medium blog: Step-by-step guide for knowledge-driven models

---

---

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.

---

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

---

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
  • Description
• 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
• bnlearn.parameter_learning
  • Description
• bnlearn.inference
  • Description
• bnlearn.bnlearn

Documentation

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

Indices and tables

• Index

• Module Index

• Search Page

---

---