Stochastic Models for CommunicationsCovers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.
Dependence in Random VectorsExplores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Elements of StatisticsIntroduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Dependence and CorrelationExplores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Markov Chains: Transition DensitiesCovers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.