Markov Chains and Applications

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Related lectures (32)

Page 3 of 4

Markov Chains: Recurrence and Transience

Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.

Dependability Evaluation in Industrial Automation

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

Lindblad equation

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

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.

Markov Chains: Applications and Analysis

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

Recurrence and Transience: Markov Chains

Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.

Invariant Distributions: Markov Chains

Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.

Markov Chains: Communicating Classes

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

Markov Chains and Algorithm Applications

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