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Related lectures (32)

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Algebraic Graph Theory: Matrices and Connectivity

Explores algebraic graph theory applied to networked control systems and consensus algorithms.

Pseudorandomness: Theory and Applications

Explores pseudorandomness theory, AI challenges, pseudo-random graphs, random walks, and matrix properties.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.

Page Rank: Theory and Convergence

Explores Page Rank algorithm theory, convergence speed, ergodic chains, spectral gap, and chain examples.

State Representation and System Dynamics

Covers the representation of state-space models and system dynamics.

Markov chains

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

Stationary Distribution in Markov Chains

Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.

Markov Chains: Definitions and Transitions

Explains Markov chains, transition matrices, and stationary distributions in random processes.

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Metropolis Hastings Algorithm: Markov Chains and Transition Matrix

Covers the Metropolis Hastings algorithm and constructing Markov chains with proposal distributions for convergence.