Lectures related to Normal distribution

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Probability Theory: Central Limit Theorem

Explores probability theory, distribution of averages, and the central limit theorem.

Multivariate Statistics: Normal Distribution

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Quantitative Risk Management: Distributions and Techniques

Covers distributions and techniques in Quantitative Risk Management for financial modeling.

Distributions and Derivatives

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Mixture models: taste heterogeneity

Explores mixture models in discrete choice and random parameters estimation results.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Estimators and Confidence Intervals

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Normal Distribution: Properties and Calculations

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Multivariate Normal Distribution: Correlation and Covariance

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.

Diffusion Models

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Compression: Prediction

Covers the concepts of compression and prediction using prefix-free codes and distributions.

Multivariate Statistics: Conditional Distributions

Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.

Linear Combinations: Moment-Generating Functions

Explores moment-generating functions, linear combinations, and normality of random variables.

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.