Maximum Likelihood Estimation

Related lectures (32)

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Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

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

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Introduces Fisher Information for parameter estimation based on collected data.

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

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.