Expected value

Related lectures (31)

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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.