Stochastic Simulation: Markov Processes Generation

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 1 of 4

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.

Markov Chains: Basics and Applications

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.

Continuous Time Markov Chains

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

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Stochastic Models for Communications

Covers the fundamentals of stochastic models for communications, focusing on Markov chains and Poisson processes.

Neurobiological Signals: Processing and Classification

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

Markov Chains: Properties and Expectations

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

Neurobiological Signal Processing

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

Stochastic Models for Communications

Covers mathematical tools for communication systems and data science, including information theory and signal processing.