Maximum Likelihood Estimation: Properties and Applications

Related lectures (31)

Page 1 of 4

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

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

Covers regression with exponential family responses using Generalised Linear Models.

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Covers the fundamental concepts of statistics and their application in data science.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.