See inference.

In general, the joint probability distribution tables are very hard to solve because it requires—for instance for binary variables—requries \(2^{n}\) entires, which is a lot.

- how do you define very large models?
- how do you perform inference with very large models
- what about the data can we use to inform the design process

“If you can tell me a generative story, we can compress our joint probability distribution”. Get ready for…… inference with causality with Baysian Network.

If you can write a program to sample from the joint probability distribution, you have just described the joint.

“Random variables are independent of causal non-descendents given their causal parents”. d-seperation