Signals & Systems II: Statistical Properties and Optimal Detectors

Related lectures (29)

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Introduces statistical modelling basics, probability theory, and key concepts for data science applications.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

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

Explores joint random variables, conditional density, and independence in probability and statistics.

Explores conditional probabilities, independence, characteristic functions, and optimal detectors for signal detection.

Covers the fundamentals of probability theory, including corollaries, conditional probability, total probability theorem, and random variables.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.