Lectures related to Identifiability

Covers Gaussian Acyclic Models focusing on linearity and identifiability.

Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Focuses on a linear system of ordinary differential equations and stability analysis.

Covers the principles and applications of linear regression, focusing on building a simple model to make suggestions.

Covers parametric estimation, seasonal modeling, Box-Jenkins methods, variance calculations, and dependence measures in time series analysis.

Explores micro assembly, emphasizing automation and precision in robotics, covering resolution, repeatability, and typical accuracy.