Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers the properties of the exponential family and the estimation of parameters.
Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
