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pgmpy is a pure python implementation for Bayesian Networks with a focus on modularity and extensibility. Implementations of various alogrithms for Structure Learning, Parameter Estimation, Approximate (Sampling Based) and Exact inference, and Causal Inference are available.

Supported Data Types

Structure Learning

Parameter Estimation

Causal Inference

Probabilistic Inference

Discrete

Yes

Yes

Yes

Yes

Continuous

Yes (only PC)

No

Yes (partial)

No

Hybrid

No

No

No

No

Time Series

No

Yes

No

Yes

Algorithms

Structure Learning

Parameter Learning

Probabilistic Inference

Causal Inference

PC with variants

Maximum Likelihood

Variable Elimination

do-operation

Hill-Climb Search

Bayesian Estimator

Belief Propagation

adjustment sets

Tree Search

Expectation Maximization

MPLP

Max-Min Hill-Climb

Sampling methods

Exhaustive Search

All example notebooks are also available at: https://github.com/pgmpy/pgmpy/tree/dev/examples All tutorial notebooks are also available at: https://github.com/pgmpy/pgmpy_notebook

Indices and tables