Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Related lectures (32)

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Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

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

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.

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

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

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

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

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

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

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