Quantitative Risk Management: Distributions and Techniques

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Related lectures (31)

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Probability and Statistics: Variance and Covariance Calculations

Discusses variance and covariance calculations, transformations of random variables, and mixtures in probability and statistics.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Describing Data: Statistics and Hypothesis Testing

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

Multivariate Statistics: Introduction and Methods

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Probability Distributions

Covers various probability distributions and their applications in real-world scenarios.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Probability Distributions: Central Limit Theorem and Applications

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Elements of Statistics

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

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