Lectures related to Heavy-Tailed Distributions

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

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Explores confidence intervals, T-test, and hypothesis testing, including assumptions and critical regions.

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

Covers probability distributions, moments, and continuous random variables.

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

Covers the theory of sampling, focusing on statistics for mathematicians.

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

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

Covers the concept of compression and constructing prefix-free codes based on given information.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

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

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Covers Levy flights, Central Limit Theorem, and Mesoscopic Master Equation with transition rates in an assurance system.

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