Mixture models: taste heterogeneity

Related lectures (29)

Page 3 of 3

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

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

Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Explores probability theory, distribution of averages, and the central limit theorem.

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

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

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

Explores statistical models, families of distributions, transformations, and their applications in probability theory.