The Likelihood of Data under a Model

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Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

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

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

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

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

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

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

Covers variational inference and neural networks for classification tasks.

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

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.