Lectures related to Probability distribution

Explores heavy-tailed distributions, the Hill estimator, convergence to Gaussian, and distribution comparison.

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

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

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.