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)