Stellar Systems Dynamics

Related lectures (31)

Page 3 of 4

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

Explores the bijection between distribution functions and moment generating functions.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Covers fluid quantities, moments of kinetic equation, two-fluid model, and plasma turbulence simulation.

Covers the properties of distribution functions, discrete and continuous random variables, independence, and notation in probability theory.

Covers loading compressed files, filtering dataframes, and analyzing data distribution for effective analysis.

Explores generating functions, Laplace transform, and their role in probability distributions.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.