Lectures related to Ronald Fisher

Sampling Distributions: Theory and Applications

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

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

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Sampling Theory: Statistics for Mathematicians

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

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.

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.

DOE Qualitative factors III

Explores qualitative factors in Design of Experiments, including Latin squares, factorial designs, and ANOVA tables.

DOE Qualitative factors: IV

Explores qualitative factors in Design of Experiments, including statistical tests and error types.

Confidence Intervals and Pivotal Quantities

Explores pivotal quantities in statistics and their role in constructing confidence intervals and hypothesis tests.

Linear Binary Classification

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.

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

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

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

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

Statistical Theory: Inference and Sufficiency

Explores statistical inference, sufficiency, and completeness, emphasizing the importance of sufficient statistics and the role of complete statistics in data reduction.

Variance and Independent Random Variables

Covers variance, independent random variables, and their properties, including examples and proofs.

Parameter Estimation & Fisher Information

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

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Maximum Likelihood Estimation

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.

Maximum Likelihood Estimation

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