Module 3: Probability & Statistics
This page aggregates the generated reference routes used by the learner-facing module.
- Semester:
semester-01-math-foundations - App:
foundations
Read only if stuck
- Introduction to Probability: Sample spaces and Pebble World
- Introduction to Probability: Naive definition of probability
- Introduction to Probability: How to count (Part 1)
- Introduction to Probability: Non-naive definition of probability (Part 1)
- MCS: Set Theory and Probability
- Introduction to Probability: Definition and intuition (Part 1)
- Introduction to Probability: Bayes' rule and the law of total probability (Part 1)
- Introduction to Probability: Independence of events (Part 1)
- Introduction to Probability: Conditioning as a problem-solving tool (Part 1)
- Introduction to Probability: Pitfalls and paradoxes (Part 1)
- MCS: The Four-Step Method for Conditional Probability
- MCS: The Law of Total Probability
- MCS: Independence
- Introduction to Probability: Random variables
- Introduction to Probability: Distributions and probability mass functions (Part 1)
- Introduction to Probability: Bernoulli and Binomial
- Introduction to Probability: Hypergeometric
- Introduction to Probability: Cumulative distribution functions
- Introduction to Probability: Functions of random variables (Part 1)
- Introduction to Probability: Geometric and Negative Binomial (Part 1)
- Introduction to Probability: Poisson (Part 1)
- MCS: Random Variable Examples
- MCS: Distribution Functions (Part 1)
- Introduction to Probability: Definition of expectation
- Introduction to Probability: Linearity of expectation (Part 1)
- Introduction to Probability: Indicator r.v.s and the fundamental bridge (Part 1)
- Introduction to Probability: LOTUS / Variance
- Introduction to Probability: Joint, marginal, and conditional (Part 1)
- Introduction to Probability: 2D LOTUS
- Introduction to Probability: Covariance and correlation (Part 1)
- MCS: Great Expectations (Part 1)
- MCS: Linearity of Expectation (Part 1)
- MCS: Chebyshev's Theorem
- Introduction to Probability: Probability density functions (Part 1)
- Introduction to Probability: Uniform
- Introduction to Probability: Normal (Part 1)
- Introduction to Probability: Exponential (Part 1)
- Introduction to Probability: Summaries of a distribution (Part 1)
- Introduction to Probability: Law of large numbers
- Introduction to Probability: Central limit theorem (Part 1)
- Introduction to Probability: Sampling and simulation / summary statistics appendix
- MCS: Estimation by Random Sampling
- MCS: Probability versus Confidence (Part 1)
Optional deep dive
- Introduction to Probability: Story proofs
- Introduction to Probability: Coherency of Bayes' rule
- Introduction to Probability: Connections between Binomial and Hypergeometric
- Introduction to Probability: Using probability and expectation to prove existence (Part 1)
- Introduction to Probability: Sums of independent r.v.s via MGFs (Part 1)
- Introduction to Probability: Multinomial (Part 1)
- MCS: Markov's Theorem