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Lecture
Markov chains
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Related lectures (32)
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Markov Chains: Theory and Applications
Covers the theory and applications of Markov chains, focusing on key concepts and properties.
Modeling Neurobiological Signals: Markov Chains
Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.
Monte Carlo Markov Chains
Covers the theory of Markov chains and Monte Carlo methods.
Lindblad equation
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Markov Chains and Algorithm Applications
Covers the application of Markov chains and algorithms for function optimization and graph colorings.
Theory of MCMC
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods
Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.
Coupling of Markov Chains: Ergodic Theorem
Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.
Monte Carlo Markov Chains
Covers Monte Carlo Markov Chains and sampling algorithms for iterative trial configurations.
Invariant Distributions: Markov Chains
Explores invariant distributions, recurrent states, and convergence in Markov chains, including practical applications like PageRank in Google.
