---
title: "SU-CS238 SEP282023"
source: https://www.jemoka.com/posts/kbhsu_cs238_sep282023/
date: 2023-09-28
---

Notation shorthand for probability Take
\begin{equation} P(X = 1) = \frac{1}{6} \end{equation}
We can write this in short hand like:
\begin{equation} P(X^{1}) = P(X=1) = \frac{1}{6} \end{equation}
\(P\) vs \(p\) Upper case \(P\) for probability mass function (one shot chance), lower case \(p\) for probability density functions (integral)
New Concepts degrees of belief and describing them using the language of probability discrete distribution and continuous distribution and joint probability distribution important tools: parameters of a distribution probability density functions cumulative distribution function quantile function fun probability distributions Gaussian distribution + Truncated Gaussian distribution uniform distribution conditional probability and Bayes Theorem unique models that leverage conditional probability conditional Gaussian models linear gaussian model conditional linear Gaussian models: use your big brain to add up 1) and 2), with continuous random variables \(X, Y\), and a discrete \(Z\), where \(p(x \mid y, z)\). sigmoid model Baysian Network and conditional independence d seperation Important Results / Claims history and impact of decision making law of total probability fun axioms belief axioms: universal comparability transitivity probability axioms: axiom of probability Methods of Compressing the Parameters of a Distribution assuming independence using a decision tree checking for conditional independence Questions
