Lectures related to Four Functions Theorem

Copulas: Properties and Applications

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Dependence Concepts and Copulas

Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Linear Regression: Pearson Correlation

Covers the Pearson correlation, relationship direction, form, strength, and regression model assessment.

Air Pollution: Correlation Analysis

Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.

Estimation and Correlation

Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.

Dependence Measures: Rank Correlations

Covers rank correlations, tail dependence, and copula fitting methods.

A Localization Tale: Practical Course by BIOP

Covers practical aspects of using Fiji for everyday tasks in biological imaging.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Quantifying Statistical Dependence: Covariance and Correlation

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Canonical Correlation Analysis: Overview

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Dimensionality Reduction: Curse of Dimensionality

Explores the curse of dimensionality, variable selection methods, coefficient of determination, and limitations of filtering techniques.

Correlation Inequalities

Explores correlation inequalities and their applications in various contexts.

Comparison Statistics: Hypothesis Testing and ANOVA

Explores comparison statistics, hypothesis testing, ANOVA, correlation, and linear regression.

Statistical Independence and Correlation: Understanding Gauss Functions

Explores statistical independence, correlation, Gauss functions, likelihood estimation, and Gaussian Mixture Models for clustering.

Partial Differential Equations and Hessians

Covers partial differential equations, Hessians, and the Implicit Function Theorem, with a focus on exam question resolution.

Ramanujan Graphs: Constructions and Similarities

Explores Ramanujan graphs' constructions, matching polynomials, perfect matchings, and universal covers, along with quantitation and qualitative aspects.

Multilinear Regression: Least Square Fit

Explores multilinear regression, variance, correlation, optimization, ANOVA, and design procedures.

Taylor's approximation: Mean Value Theorem, l'Hopital rule, Examples

Covers the Mean Value Theorem, l'Hopital rule, Taylor's approximation, and more.