Stochastic Models for Communications: Discrete-Time Markov Chains - First Passage Time

Related lectures (29)

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Covers examples of absorbing Markov chains in discrete time.

Discusses discrete-time Markov chains and absorption time in communication systems.

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.