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Related lectures (31)

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Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Probability & Stochastic Processes

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

Continuous Time Markov Chains

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Markov Chains and Applications

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.

Bonus Malus System: Transition Probabilities

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

Hidden Markov Models: Primer

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.