Belief Propagation in Stochastic Block Models

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Covers the likelihood ratio test for detection and estimation in statistical analysis.

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

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

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.

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

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.