COM-516: Markov chains and algorithmic applications

Lectures in this course (34)

Discusses the sharp transition in mixing time of a random walk.

Explores proof ideas related to the cut-off phenomenon and its implications in random walks on hypercubes.

Covers the basics of Markov chains and their algorithmic applications.

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 the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

Explores Markov chains, focusing on the coloring problem and algorithm analysis.

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

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

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

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

Explores the application of Markov chains in algorithms and the theorems guaranteeing good representations.

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.

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