Designing Experiments and Mastering Uncertainty — Part I
Probability, random variables, and Bayesian reasoning
"Any measurement, without knowledge of the uncertainty, is meaningless." — Walter Lewin, MIT
"Those who are able to represent what they do not know make better decisions." — Chris Piech, CS109 class at Stanford
The Series
1 — Probability and Bayes
Foundations of probability, conditional probability, and Bayes' theorem. What is a probability? How do we update beliefs with evidence?
2 — Random Variables
Random variables, expectation, variance, and key distributions. The Binomial distribution and the Normal distribution.
Course Reader
The reference material for this part is the open textbook Probability for Computer Scientists by Chris Piech (Stanford CS109) — from "Probability" to "Bayes' Theorem" (included), "Many Coin Flips", and Part 2 up to "Binomial Distribution" (included) and "Normal Distribution".
Continue to Part II →
Estimators, the Central Limit Theorem, and bootstrapping