---
title: "General Inference"
type: concept
related: [Inference, Baysian Network, Joint Probability Distribution]
source: https://www.jemoka.com/posts/kbhgeneral_inference/
confidence: high
status: active
---

See inference.
In general, the joint probability distribution tables are very hard to solve because it requires&mdash;for instance for binary variables&mdash;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 &ldquo;If you can tell me a generative story, we can compress our joint probability distribution&rdquo;. Get ready for&hellip;&hellip; 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.
&ldquo;Random variables are independent of causal non-descendents given their causal parents&rdquo;. d-seperation
