EM Algorithm: Lite-bulbs Lifetime Estimation

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

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Covers the normal distribution, including its properties and calculations.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

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

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

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

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

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.