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Related lectures (32)

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Distribution Estimation

Covers the estimation of distributions using samples and probability models.

Distribution Estimation

Covers the estimation of distributions using various methods such as minimum loss and expectation.

Sampling Distributions: Theory and Applications

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

Basic Principles of Point Estimation

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Multivariable Control: Observers and Controllers

Covers the design of reduced-order observers and output feedback controllers in multivariable control systems.

Confidence Intervals: Gaussian Estimation

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Estimator of Variance

Explores variance estimation, creating personal estimators, correcting bias, and understanding Mean Square Error in statistical analysis.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Error Estimation in Numerical Integration

Explains error estimation in numerical integration, focusing on completeness and accuracy.