1. Introduction: What is Probabilistic Programming and why Pyro?
  2. A Simple but Complete Example
  3. Modelling in Pyro
  4. Minipyro (Model Sampling) and Event Handlers
    1. trace
    2. replay
    3. block
    4. condition
    5. do
  5. Tensor Shapes and Plate - Multi-Dimensional Inputs and Outputs
  6. Sampling and Parameterization
  7. The Funsor Backend
  8. Getting Probabilities From Your Model
  9. The Guide or Approximate Distribution
    1. Manual Guide
    2. Automatic Guide
  10. Minipyro (Inference)
  11. Posterior Inference
    1. Importance Sampling
    2. The ELBO
    3. SVI
  12. The Posterior Predictive Distribution
  13. Neural Networks in Pyro- PyroModule
  14. The Structural Cause Model and Writing a DAG
  15. Training the Structural Causal Model
  16. Intervention and The Interventional Distribution
  17. Calculating Causal Effects
  18. Counterfactual Inference
    1. Abduction
    2. Intervention
    3. Inference
  19. The Autoencoder
  20. The Variational Autoencoder
  21. The Causal Effect Variational Autoencoder