# Probability MOC ## Contents 1. [Sample Space](Sample%20Space.md) 2. [Probability Model](Probability%20Model.md) 3. [Random Variable](Random%20Variable.md) 4. [Probability Density](Probability%20Density.md) 5. [Probability Density Function](Probability%20Density%20Function.md) 6. [Cumulative Distribution Function](Cumulative%20Distribution%20Function.md) 7. [Probability-Inequalities](Probability-Inequalities.md) 8. [Expected Value](Expected%20Value.md) 9. [Variance](Variance.md) 10. [Bernoulli Distribution](Bernoulli%20Distribution.md) 11. [Bernoulli Distribution and Trials](Bernoulli%20Distribution%20and%20Trials.md) 12. [Binomial Distribution](Binomial%20Distribution.md) 13. [Stochastic Processes](Stochastic%20Processes.md) 14. [Laws of Large Numbers](Laws%20of%20Large%20Numbers.md) 15. [Central Limit Theorem](Central%20Limit%20Theorem.md) 16. [[Support (probability)]] 17. [Abstract Probability Distributions](Abstract%20Probability%20Distributions.md) 18. [Conditional Probability](Conditional%20Probability.md) ### Notes and Ideas I have gleamed over time * Taleb described randomness nicely: Incomplete information is functionally equivalent to a random process. * "For example, we all feel that we understand flipping a coin or rolling a die, but still accept randomness in each outcome. The theory of probability was developed particularly to give precise and quantitative understanding to these types of situations." * This effectively touches on the distinction between **epistemic** and **aleatoric** uncertainty. --- Date: 20211007 Links to: Tags: References: * Discrete Stochastic Processes Notes, Gallagher ([here](https://drive.google.com/file/d/1DsCW0L8lLt6YdF2SBNK73UJh1oUcerwQ/view?usp=sharing)) *