PGM

Probabilistic graphs: Representation, Learning, and Inference

Features

• [x] Representation
  • [x] Bayesian Network Representation
  • [x] Linked List BN Representation
  • [x] Linked List MN Representation
  • [ ] Conditional Estimation
  • [ ] Marginal Estimation
  • [ ] Joint Estimation
• [x] Inference
  • [x] Metropolis-Hastings algorithm
  • [x] Gibbs Sampling on 2d grid
  • [ ] Generalized Gibbs Sampling
  • [x] Message Parsing and BP
  • [x] Loopy BP
  • [x] VE
  • [ ] Causal Interventions
• [x] search methods
  • [x] DFS
  • [x] BFS
• [x] Additional
  • [x] Finding Active Trails
  • [ ] Max clique size and clique node
  • [ ] Calculate tree-width
• [ ] Learning
• [x] Miscellaneous
  • [x] Random BN and MN generation

Installation

pip install ppgm

Contact

• Avinash Kori (koriavinash1@gmail.com)