Probabilistic Estimation in Spin Glass Card Game

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Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.

Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.

Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Explains estimation through mean-squared error and Fisher information in the context of adaptive filters and exponentiated distributions.

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.