Covers continuous random variables, probability density functions, and distributions, with practical examples.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.
Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.
