Signal Detection Theory

Related lectures (28)

Page 2 of 3

Covers probability, statistics, independence, covariance, correlation, and random variables.

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

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

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

Explores conditional density, expectations, and independence of random variables with practical examples.

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

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