Lectures related to Fisher information

Estimation: Mean-Squared Error and Fisher Information

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

Parameter Estimation: Detection & Estimation

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

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Confidence Intervals: Gaussian Estimation

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

Estimation: Linear Estimator

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

Maximum Likelihood, MSE, Fisher Information, Cramér-Rao Bound

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

Sampling Distributions: Estimators and Variance

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

ANOVA: Partitioning Total SS

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.

Quantum Sensing: Parameter Estimation and Bounds

Covers parameter estimation in quantum metrology, Quantum Fisher Information, Ramsey interferometry, entanglement, and noise impact on NooN states.

Estimation: Measures of Performance

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.

Sampling Theory: Statistics for Mathematicians

Covers the theory of sampling, focusing on statistics for mathematicians.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

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

Maximum Likelihood Estimation: Theory

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

Raster - Vector: Spatial Interactions

Explores combining raster and vector layers for spatial analysis in QGIS.

Fisher Information, Cramér-Rao Inequality, MLE

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

Maximum Likelihood Estimation: Econometrics

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

Parameter Estimation & Fisher Information

Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Sampling Theory: Sufficiency and Ancillarity

Explores sufficiency and ancillarity in sampling theory, emphasizing the importance of sufficient statistics in compressing data without losing information.