Probabilistic Analysis: Hiring Problem

Related lectures (31)

Page 3 of 4

Covers conditional probability distributions and introduces the concept of conditional expected value.

Covers random variables, variances, and covariance, as well as the probability in random graphs.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Covers a quiz on advanced probability concepts and calculations.

Covers the Geometric Distribution, biased coins, expected number of flips, and generating random numbers.

Explores continuous random variables, cdf, pdf, convolution, and sum of dice.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Covers the theory of Brownian motion, diffusion, and random walks, with a focus on Einstein's theory for one-dimensional motion.

Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.

Covers Markov's and Chebyshev's Inequalities for random variables and probability distributions.