Markov Processes & Diffusion: Textbook Reference

In summary, a Markov process is a stochastic process where the future state of a system only depends on the current state, not on previous states. It is simpler and more efficient than traditional stochastic processes, as it only considers the current state. Diffusion, or the random movement of particles, is a type of Markov process. Markov processes have various real-world applications, such as modeling stock prices and predicting weather patterns. "Stochastic Processes and Diffusion: Theory and Applications" by P. Berglund and E. Scheinerman is a highly recommended textbook for learning about Markov processes and diffusion.
  • #1
eXorikos
284
5
This semester I have a course on mathematical methods in physics. It's in three parts and the first professor is talking about Markov processes (discrete and continuous time) and diffusion. The problem is he doesn't have any notes or a reference textbook.

Do you know any textbook on these topics?
 
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  • #2
Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 2nd Edition

That's the one we use at my school; it's pretty friendly.
 

Related to Markov Processes & Diffusion: Textbook Reference

1. What is a Markov process?

A Markov process is a type of stochastic process where the future state of a system depends only on the current state, and not on any previous states. It is based on the principle of memorylessness, meaning that the past history of the system is not relevant to predicting its future behavior.

2. How is a Markov process different from a traditional stochastic process?

A traditional stochastic process takes into account the entire history of a system to predict its future behavior, whereas a Markov process only considers the current state. This makes Markov processes simpler and more efficient to model and analyze, especially for complex systems.

3. What is diffusion in the context of Markov processes?

Diffusion refers to the random movement of particles or molecules in a system. In the context of Markov processes, diffusion is a type of stochastic process where the state of a system changes due to the random movement of its particles.

4. What are some real-world applications of Markov processes?

Markov processes have a wide range of applications in various fields, such as finance, biology, and physics. Some examples include modeling stock prices, predicting weather patterns, analyzing protein folding, and simulating traffic flow.

5. What is a good textbook reference for learning about Markov processes and diffusion?

One highly recommended textbook is "Stochastic Processes and Diffusion: Theory and Applications" by Paul L. Berglund and Edward Allen Scheinerman. It provides a comprehensive introduction to Markov processes and diffusion, with clear explanations and examples, making it suitable for both students and researchers.

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