Implicit Generative Models

Related lectures (30)

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Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Covers the estimation of distributions using samples and probability models.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

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

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

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.