Stochastic Process prerequsites and difficulty?

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SUMMARY

The university course "Stochastic Process" requires a foundational understanding of Probability, specifically utilizing Rosen's textbook. However, discussions indicate that a deeper knowledge of Functional Analysis and Measure Theory is essential for grasping the course content effectively. Key topics covered include Random Walks, Markov Chains, Poisson Processes, and Graph Theory, suggesting a multidisciplinary approach. The absence of an announced instructor limits direct inquiries regarding specific prerequisites.

PREREQUISITES
  • Probability theory using Rosen's textbook
  • Functional Analysis
  • Measure Theory
  • Graph Theory
NEXT STEPS
  • Study Functional Analysis concepts relevant to Stochastic Processes
  • Explore Measure Theory applications in probability
  • Research Random Walks and their mathematical implications
  • Investigate Markov Chains and their use in stochastic modeling
USEFUL FOR

Students considering the "Stochastic Process" course, mathematicians interested in advanced probability topics, and educators developing curriculum in stochastic analysis.

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My university is offering a course called "Stochastic Process". The only prerequisites to this course according to my university is a course in Probability which uses the book by Rosen.

I've read elsewhere that the course actually requires more of analysis (functional analysis and measure theory) than probability.

The course briefly mentions

Random walks, Markov chains, Poisson processes, graphs


There is no instructor announced, so I cannot ask him or her about the course. The word "graph" suggests there is also graph theory involved.

So does anyone know the prerequisites based on the description? For some reason, none of those terms in the description seem to have anything to do analysis.
 
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