tbnpy documentation

Contents:

• Overview
  • TBNPy: Python toolkit for tensor-based Bayesian network (TBN)
  • What TBNpy is for
  • TBNpy workflows
• Repository structure
  • Core library (tbnpy/)
  • Examples (examples/)
• tbnpy
  • tbnpy package
• EMA aftershock example
  • Step 1: define the model
  • Step 2: forward inference
  • Step 3: conditioning on component failure
  • Computational cost scaling
• ABCDE example
  • Model structure
  • Task
  • Step 1: defining variables and probability models
  • Step 2: evidence and scalable inference
  • Prior vs posterior distributions