Markov Chains Decomposition

Related lectures (29)

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Explores convergence in law for random variables, including Kolmogorov's theorem and proofs based on probability lemmas.

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

Covers the (s,s)-inventory model, stationary Markov chains, time reversal property, and detailed balance condition.

Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.

Explains inventory cost flows, manufacturing vs. merchandising activities, and cost accounting principles.

Explores the balance between exploration and exploitation in multi-arm bandit algorithms.

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.

Covers the Laplace equation, decomposition of linear problems, and solutions through separation of variables.

Covers the basics of supply management and different aggregate planning strategies.