Maximum Likelihood Estimation

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Related lectures (31)

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Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Likelihood Ratio Tests: Optimality and Extensions

Covers Likelihood Ratio Tests, their optimality, and extensions in hypothesis testing, including Wilks' Theorem and the relationship with Confidence Intervals.

Decision Theory: Risk and Hypothesis Testing

Covers decision theory, risk functions, and hypothesis testing in statistical inference.

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Estimators and Confidence Intervals

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

L-Moment Estimation: Probability-Weighted Moments

Covers L-moment estimation, probability-weighted moments, and maximum likelihood inference basics.

Maximum Likelihood Estimation: Theory and Examples

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Sampling Distributions: Theory and Applications

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

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.