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Lecture
Stochastic Models for Communications
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Related lectures (31)
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Ergodic Theory: Markov Chains
Explores ergodic theory in Markov chains, discussing irreducibility and unique stationary distributions.
Markov Chains: Theory and Applications
Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.
Markov Chains: Simulation and Optimization
Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.
Markov Chains: Introduction and Properties
Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.
Ergodic Theorem: Proof and Applications
Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.
Length of Curves and Tangent Vectors
Covers the calculation of the length of curves and the properties of tangent vectors.
Stochastic Models for Communications
Covers the fundamentals of stochastic models for communications, focusing on Markov chains and Poisson processes.
Continuous-Time Markov Chains: Kolmogorov Equations
Covers continuous-time Markov chains and Kolmogorov equations in stochastic communication models.
Continuous-Time Markov Chains: Kolmogorov Equations
Covers the equations of Kolmogorov for continuous-time Markov chains.
Meromorphic Functions & Differentials
Explores meromorphic functions, poles, residues, orders, divisors, and the Riemann-Roch theorem.
