Heavy-Tailed Distributions

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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.