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Lecture
Markov Chains and Applications
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Related lectures (36)
Markov Chains: Recurrence and Transience
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Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.
Probability & Stochastic Processes
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Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.
Markov Chains: Ergodic Chains Examples
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Covers stochastic models for communications, focusing on discrete-time Markov chains.
Markov Chains: Ergodic Chains Examples
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Covers stochastic models for communications, focusing on discrete-time Markov chains.
Dependability Evaluation in Industrial Automation
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Explores dependability evaluation, preventive maintenance, reliability, Markov models, FMEA, FTA, and software safety integrity in industrial automation.
Neurobiological Signals: Processing and Classification
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Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.
Stochastic Processes: Markov Chains
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Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.
Invariant Distributions: Markov Chains
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Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.
Markov Chains: Ergodic Chains Examples
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Covers stochastic models for communications, focusing on discrete-time Markov chains.
Modeling Neurobiological Signals: Markov Chains
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Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.
Recurrence and Transience: Markov Chains
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Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.
Markov Chains: State Classification
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Covers the classification of states in discrete-time Markov chains.
Markov Chains: Theory and Applications
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Covers the theory and applications of Markov chains, focusing on key concepts and properties.
Maxwell's Equations in Polarizable Matter
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Covers the review of Maxwell's equations in polarizable matter and the concept of internal/demagnetization/stray field.
Learning from the Interconnected World with Graphs
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Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.
Experimental Techniques: NMR & muSR
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Explores how muon spin rotation measures local magnetic fields in crystals.
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