Lectures related to Dependence Measures: Rank Correlations

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

Explores copulas, rank correlations, and tail dependence measures in risk management.

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

Describing Data: Statistics and Hypothesis Testing

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

Copulas: Dependence Structures and Simulation

Covers copulas, dependence structures, simulation techniques, and properties of copula densities.

Linear Regression: Pearson Correlation

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

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.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Elements of Statistics: Estimation & Distributions

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Statistical Independence and Correlation: Understanding Gauss Functions

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

Quantitative Risk Management: Copulas and Generative Adversarial Networks

Explores copulas, simulation algorithms, fitting data with rank correlations, and GANs for image generation.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Air Pollution: Correlation Analysis

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

Principal Component Analysis: Introduction

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Signal Processing Fundamentals

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

Copulas: Dependence Modeling

Covers copulas, Sklar's Theorem, types of copulas, and simulation of copulas for risk management.

Estimation and Correlation

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

Probability Models: Fundamentals

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.