Markov Chains Decomposition

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Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Neurobiological Signals: Processing and Classification

Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.

Equilibrium of Markov Chains

Explores equilibrium in Markov Chains, covering invariant distributions, properties determination, and practical applications.

Asymptotic Behavior of Markov Chains

Explores recurrent states, invariant distributions, convergence to equilibrium, and PageRank algorithm.

Markov Chains and Applications

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

Markov Chains: Transition Probabilities

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

Dependability Evaluation in Industrial Automation

Explores dependability evaluation, preventive maintenance, reliability, Markov models, FMEA, FTA, and software safety integrity in industrial automation.

Markov Chains: Communicating Classes

Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.

Panel data: dynamic model with panel effects

Covers the Dynamic Markov model with panel effects, addressing initial condition and endogeneity.