Model Selection: AIC and BIC

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Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

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

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

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Discusses Bayesian inference for the mean of a Gaussian distribution with known variance, covering posterior mean, variance, and MAP estimator.

Covers the properties of the exponential family and the estimation of parameters.

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

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

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