Observed information

Related lectures (23)

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

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Introduces Fisher Information for parameter estimation based on collected data.

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

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