Phase 4: Math → Code Bridge · ~75 minutes · Python
Stochastic Processes
Randomness with structure. The math behind random walks, Markov chains, and diffusion models.
Hiring signal: Understanding of stochastic processes internals
What you will learn
- Simulate 1D and 2D random walks and verify the sqrt(n) scaling of displacement
- Build a Markov chain simulator and compute its stationary distribution via eigendecomposition
- Implement Metropolis-Hastings MCMC and Langevin dynamics for sampling from target distributions
- Connect the forward diffusion process to Brownian motion and explain how the reverse process generates data
Introduction
Type: Learn Language: Python Prerequisites: Phase 1, Lessons 06-07 (probability, Bayes) Time: ~75 minutes
Learning Objectives
- Simulate 1D and 2D random walks and verify the sqrt(n) scaling of displacement
- Build a Markov chain simulator and compute its stationary distribution via eigendecomposition
- Implement Metropolis-Hastings MCMC and Langevin dynamics for sampling from target distributions
- Connect the forward diffusion process to Brownian motion and explain how the reverse process generates data
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Connections, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.
Browse all courses · View pricing · DeVenture Academy