Lectures related to Generalized normal distribution

Mixture models: taste heterogeneity

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

Estimation and Confidence Intervals

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

Physics Assistantship: Electromagnetism Coordination

Covers the coordination of physics assistantship for electromagnetism course.

Variance Reduction Techniques: Antithetic Variables & Importance Sampling

Explores variance reduction techniques like antithetic variables and importance sampling in Monte Carlo estimation.

Multivariate Gaussian Distribution

Covers the definition of multivariate Gaussian distribution and its properties, including moment generating function and linear combinations of variables.

Poisson Process Mapping

Explains how q = rw defines a Poisson process and its intensity.

Generalized Linear Models II: GLM Extensions

Covers advanced topics in Generalized Linear Models, focusing on link functions, error distributions, and model interpretation.

Normal Distribution: Basics and Applications

Covers the basics of the normal distribution and its applications in probability calculations.

Optimization and Simulation

Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.

Normal Distributions

Covers the characteristics and importance of the normal distribution in statistical analysis.

Master's in Mechanical Engineering Structure

Explains the structure of the master's in mechanical engineering program, including master project and internship options.

Confidence Intervals: Student, Asymptotic Wald

Covers confidence intervals for Gaussian means, Student distribution, and Wald confidence intervals for maximum likelihood estimators.

Principal Component Analysis: Theory and Applications

Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.

Image Processing II: Gaussian Classifiers and Neural Networks

Explores Gaussian classifiers, texture classification, parameter estimation, supervised learning, and deep neural networks in image processing.

Landscape Metrics

Covers landscape metrics used in landscape ecology to characterize landscapes based on spatial patterns and ecological processes.

Probability and Statistics

Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.

Generative Learning Algorithms

Explores generative learning algorithms, decision rules, and Gaussian distribution properties in machine learning.

Monte-Carlo Integration

Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.

Scale-Free Networks: Power Laws and Preferential Attachment

Explores scale-free networks, power laws, preferential attachment, and network assortativity.

Portfolio Optimization: Models and Strategies

Explores portfolio optimization models and strategies under uncertainty, emphasizing decision criteria like value-at-risk and mean-variance functional.