Markov Chain Monte Carlo

Related lectures (32)

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Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Explores conditional expectation, the grouping lemma, and the law of large numbers.

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

Covers Markov chains, focusing on reversibility, convergence, ergodicity, and applications.

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

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.