Stochastic Processes: Markov Chains

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Distributions and Derivatives

Covers distributions, derivatives, convergence, and continuity criteria in function spaces.

Discrete-Time Markov Chains: Definitions

Covers the definitions and state probabilities of discrete-time Markov chains.

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Markov Chains: Homogeneous Processes and Stationary Distributions

Explores Markov chains, focusing on homogeneous processes and stationary distributions, with practical exercises.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Markov Chain Games

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Markov Chains: Transition Densities

Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains, focusing on key concepts and properties.

Stochastic Simulation: Markov Chains and Metropolis Hastings

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.