Complex normal distribution

Related lectures (31)

Page 1 of 4

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Covers the multivariate normal distribution, properties, and sampling methods.

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

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

Explains variational inference, Jensen's inequality, E-step, M-step, and MCMC sampling.

Covers the theory of sampling, focusing on statistics for mathematicians.

Explores vector operations in 3D space, including plane intersections and distance calculations.

Covers Cramer's theorem and Hoeffding's inequality in the context of the large deviations principle.