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Lecture
Recurrence and Transience: Markov Chains
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Related lectures (32)
Markov Chains: Recurrence and Transience
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Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.
Markov Chains: Theory and Applications
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Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.
Discrete-Time Markov Chains: Reversible Chains
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Covers reversible discrete-time Markov chains in communication models.
Probability & Stochastic Processes
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Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Invariant Distributions: Markov Chains
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Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.
Birth & Death Chains: Analysis & Probabilities
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Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.
Bonus Malus System: Transition Probabilities
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Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.
Markov Chains Decomposition
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Covers Markov chains decomposition, LLN proof, Inventory Model application, and average costs.
Dependability Evaluation in Industrial Automation
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Explores dependability evaluation, preventive maintenance, reliability, Markov models, FMEA, FTA, and software safety integrity in industrial automation.
Probability Inequalities
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Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.
Markov Chain Games
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Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.
Markov Chain Convergence
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Explores Markov chain convergence, emphasizing invariant distribution, Law of Large Numbers, and mean rewards computation.
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