Discrete Dynamical Systems

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Description

This lecture covers the concept of discrete dynamical systems, focusing on eigenvalues, determinants, traces, and stochastic matrices. It explains the behavior of sequences, prey-predator systems, Markov chains, and long-term system analysis.

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Related lectures (31)

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Related lectures (31)

Spectral Gap in Markov Chains

Explores the spectral gap in Markov chains and its impact on convergence speed.

Stationary Distribution in Markov Chains

Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.

Applied Probability & Stochastic Processes

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

Eigenvalues and Eigenvectors of Markov Chains

Explores eigenvalues and eigenvectors of Markov chains, focusing on convergence rates and matrix properties.

Markov Chain Analysis

Delves into Markov chains by analyzing a scenario with two fleas moving in opposite directions, exploring transition matrices and probabilities over time.