Stochastic Processes
Probability & Statistics
Master probability theory for random processes, Markov chains, Brownian motion, martingales, and Ito calculus.
Learning Objectives
- Analyze Markov chains and their stationary distributions
- Understand Brownian motion and Wiener processes
- Apply Ito's lemma to stochastic differential equations
- Model financial derivatives using stochastic calculus
Lessons
1
Probability Spaces & Random Variables
35 min
2
Markov Chains: Discrete Time
35 min
3
Markov Chains: Continuous Time & Poisson Processes
35 min
4
Brownian Motion & Wiener Process
35 min
5
Martingales & Optional Stopping
35 min
6
Stochastic Differential Equations & Ito's Lemma
35 min
7
Applications: Black-Scholes & Filtering
35 min
Quick Practice
Test your knowledge with a quick interactive challenge from this module.
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Key Concept Flashcards
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