L20.2 Overview of the Classical Statistical Framework

L20.3 The Sample Mean and Some Terminology

Lecture 1: Pigeonhole Principle

L20.4 On the Mean Squared Error of an Estimator

L26.9 Gambler's Ruin

MIT RES.6-012 Introduction to Probability, Spring 2018

L21.3 Stochastic Processes

MIT 18.200 Principles of Discrete Applied Mathematics, Spring 2024

L21.5 The Fresh Start Property

L20.9 Maximum Likelihood Estimation

L01.1 Lecture Overview

L21.7 The Time of the K-th Arrival

L22.8 The Fresh Start Property and Its Implications

Lecture 3: Inclusion-Exclusion

Lecture 2: Independence and Conditioning

L21.8 Merging of Bernoulli Processes

L20.8 Other Natural Estimators

L22.6 A Simple Example

Lecture 12: Introduction to Linear Programming

L21.2 The Bernoulli Process

Lecture 9: Chernoff Bounds