Covers Monte-Carlo integration, simulation, and transforming draws from different distributions.
Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.
Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
