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Related lectures (31)

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Covers the basics of probability theory, random variables, joint probability, and inference.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Introduces weighted mean calculation and radioactive decay concept with probability estimation.

Discusses the steady-state Kalman predictor, provides examples, and compares Luenberger and Kalman filtering.

Covers the theory behind Gaussian processes and the design of receivers using MAP calculations.

Covers t-tests, p-values calculation, and comparison of coefficients.

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.