Concept

Expected value

Related lectures (31)

Introduces random variables, probability distributions, and expected values through practical examples.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Covers advanced probability concepts, including random variables and expected value calculation.

Explores the probabilistic analysis of the hiring problem, focusing on strategies, expected hires, and randomized algorithms.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

Explores advanced probability concepts, independence of events, expected value theorem, and inversions in permutations.

Covers the proof of the linearity of expectations for independent random variables and discusses properties of expected value.

Explores the Theorem of the Average, integrability criteria, properties of the integral, and volume concepts.

Explores probabilistic analysis of the hiring problem using Indicator Random Variables and clever methods to calculate expected outcomes.

Explores Hoeffding's inequality and the binomial distribution, focusing on error minimization and generalization gaps in predictor selection.

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

Explores expectation value, convex functions, weights, and inequalities in mathematical analysis.

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

Explores mixture models and individual-level parameters in discrete choice scenarios, covering distribution, Bayes theorem, and expected values.

Discusses the linearity of expectations theorem and inversions in permutations.

Covers the basics of discrete probability, including notations, axioms, pmf, examples, expectation, variance, and indicator variables.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Explores expected value in probability theory, including dice rolls and Bernoulli trials.