Advanced Topic 47 of 51 Math for Computing
Stochastic Processes
Collections of random variables evolving over time.
How it works
A family of random variables indexed by time: random walks, Poisson processes, Brownian motion, Markov processes.
Where it is used
Modeling queues, stock prices.
Common pitfall
Assumptions such as stationarity must be checked.
Where it fits in the course
Stochastic Processes is topic 47 of 51 in Math for Computing, rated Advanced. It follows Monte Carlo Methods. The next topic is Maximum Likelihood Estimation.
Practice and explore
- Open the interactive lesson, with a mini quiz.
- Take the Math for Computing quiz.
How to study this topic
- Read the explanation above and restate the definition in your own words.
- Open the interactive lesson and answer its mini quiz.
- Read the common pitfall, then write a short example that avoids it.
- Check your understanding in the Math for Computing quiz, and revisit the topic if you miss a question.
Frequently asked questions
What is Stochastic Processes?
Collections of random variables evolving over time. It belongs to Math for Computing and is an advanced-level topic.
Where do people use Stochastic Processes?
Modeling queues, stock prices.
What is the most common mistake with Stochastic Processes?
Assumptions such as stationarity must be checked.
What should I learn before this topic?
Start with Monte Carlo Methods, the topic just before it in Math for Computing.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Math for Computing quiz.
Is Stochastic Processes hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn Stochastic Processes?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Stochastic Processes?
Monte Carlo Methods, Maximum Likelihood Estimation, Markov Chains are closely connected. The full list is in the related topics section of this page.
What should I study after Stochastic Processes?
Continue with Maximum Likelihood Estimation, the next topic in Math for Computing.
Related topics
- Monte Carlo Methods (Math for Computing)
- Maximum Likelihood Estimation (Math for Computing)
- Markov Chains (Math for Computing)
- Bayesian Inference (Math for Computing)
- Processes (Operating Systems)
- Communicating Sequential Processes (Concurrency & Parallelism)