Concept

Pairwise independence

Related lectures (13)

Conditional Probability: Understanding Independence

Explores conditional probability and independence between events, using dice roll examples to illustrate fundamental probability concepts.

Probability Basics: Theory and Applications

Covers basic probability theory, conditional probability, independence, and Bayes' Theorem.

Conditional Probability: Part B

Covers conditional probability and independence of events using family examples.

Lovász Local Lemma: Basics

Covers the basics of the Lovász Local Lemma, including mutually independent bad events and pseudoprobabilities.

Probability Theory: Independence and Conditional Independence

Explores independence and conditional independence in probability theory through mathematical definitions and examples.

Probability Distributions: Basics

Introduces probability distributions, uniform distribution, probabilities of events, complements, unions, and disjoint events.

Pairwise Independence: Hashing and Load Balancing

Explores pairwise independence in hashing to avoid collisions and achieve load balancing.

Probability: Independence

Explores the concept of independence in probability theory, showing how events can occur without influencing each other.

Multivariate Statistics: Normal Distribution

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Propagation of Uncertainty II

Explores the propagation of uncertainty in correlated variables and extreme correlations, Tchebychev inequality, confidence intervals, and Taylor series development.

Entropy and Algorithms: Applications in Sorting and Weighing

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Regression Modeling: Variable Selection

Explores regression modeling, emphasizing the importance of interpretation and prediction in model building.

Probability and Statistics

Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.