Markov Chains: Simulation and Optimization

Related lectures (32)

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

Covers hydrologic parameters optimization using RS MINERVE software and analyzes the best solution.

Explores the motivation and algorithm behind the Monte Carlo Chain method.

Covers Markov chains, Monte Carlo methods, low discrepancy sequences, and multidimensional integrals computation.

Discusses optimization vs. simulation in energy systems engineering, district heating networks, and scientific writing guidelines.

Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.

Explores greedy heuristics, neighborhoods in optimization, and local search algorithms.

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

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

Explores Biased Monte Carlo Markov Chain, including Bayes-optimal estimation and Metropolis-Hastings algorithm.