Panel data: dynamic model with panel effects

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

Covers Markov chains, Metropolis algorithm, Glauber dynamics, and heat bath dynamics.

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

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

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.

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

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

Explores quantum entropy in Markov chains and Bell states, emphasizing entanglement.

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.