Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Covers the properties of the exponential family and the estimation of parameters.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.
Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.
