Probability
Probability Basics
Random Variables
Probability Density Function
Normal Distribution and Gaussian Function
Multivariate Distribution
Joint Distribution
If we have two different RVs representing two different events \(X\) and \(Y\), then we represent the probability of two distinct events \(x \in \mathcal{X}\) and \(y \in \mathcal{Y}\) both happening, which we will denote as follows:
\[
p(X=x AND Y=y) = p(x,y)
\]
The function \(p(x,y)\) is called joint distribution.
Independence
Conditional Probability
Theorem of Total Probability and Marginal Distribution
Bayes Rule
Conditional Independence
Moments of RVs
Entropy
Resources
- Duckietown Preliminaries
- Probabilistic Robotics, Thrun, Burgard, Fox (Amazon.com, Amazon.de, affiliate links)
- Papoulis: Probability, Random Variables, Stochastic Processes (affiliate link)
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