Probabilistic Analysis: The Hiring Problem

Related lectures (30)

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Covers randomness and information in biological data, focusing on discrete random variables and their quantification.

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

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

Covers conditional expectation, convergence of random variables, and the strong law of large numbers.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Explores random walks, the Moran process, and examples in population genetics, protein abundance modeling, and bacterial chemotaxis.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.