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--- title: "General Inference" source: https://www.jemoka.com/posts/kbhgeneral_inference/ --- 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