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Related lectures (32)

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Explores the PageRank algorithm used by Google to rank web pages.

Covers the estimation of relaxation time in chains and the importance of sample sizes.

Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.

Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

Explores the interconnected world of networks, from people to digital platforms.

Explores anchor text indexing, PageRank, and link-based ranking using a random walker model.

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Covers the creation of links using the tag in HTML.

Covers stochastic simulation and variance reduction techniques, focusing on Courra variate and auxiliary distribution generation.

Covers the practical computation of PageRank using iterative methods and the challenges of the eigen-vector method.