Markov Chains: Basics and Applications

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Related lectures (32)

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Generation of Markov Processes

Covers the generation of Markov processes and Markov chains, including transition matrices and stochastic matrices.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Continuous Time Markov Chains

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

Stochastic Simulation: Markov Processes Generation

Covers the generation of Markov processes and Poisson processes in stochastic simulation.

Markov Chains: Theory and Applications

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Neurobiological Signal Processing

Explores neurobiological signal processing, covering spike modeling, signal classification, and data characterization using principal component analysis.

Neurobiological Signals: Processing and Classification

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

Discrete-Time Markov Chains: Definitions

Covers the definitions and state probabilities of discrete-time Markov chains.

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Markov Chains: Properties and Expectations

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.