Graphical model analysis toolbox.
Project description
PGM
Probabilistic graphs: Representation, Learning, and Inference
Features
- Representation
- Bayesian Network Representation
- Linked List BN Representation
- Linked List MN Representation
- Conditional Estimation
- Marginal Estimation
- Joint Estimation
- Inference
- Metropolis-Hastings algorithm
- Gibbs Sampling on 2d grid
- Generalized Gibbs Sampling
- Message Parsing and BP
- Loopy BP
- VE
- Causal Interventions
- search methods
- DFS
- BFS
- Additional
- Finding Active Trails
- Max clique size and clique node
- Calculate tree-width
- Learning
- Miscellaneous
- Random BN and MN generation
Installation
pip install ppgm
Contact
- Avinash Kori (koriavinash1@gmail.com)
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Source Distribution
ppgm-0.0.4.tar.gz
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