1. 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