UsefulLinks
Statistics
Stochastic Processes
1. Foundations of Probability Theory
2. Introduction to Stochastic Processes
3. Discrete-Time Markov Chains
4. Poisson Processes
5. Continuous-Time Markov Chains
6. Renewal Theory
7. Martingales
8. Brownian Motion
9. Stochastic Calculus
10. Stationary Processes
11. Applications in Queueing Theory
12. Applications in Finance
13. Applications in Biology and Population Dynamics
14. Applications in Physics and Engineering
8.
Brownian Motion
8.1.
Definition and Construction
8.1.1.
Standard Brownian Motion
8.1.1.1.
Axioms and Properties
8.1.1.2.
Gaussian Process Structure
8.1.2.
Construction Methods
8.1.2.1.
Limit of Random Walks
8.1.2.2.
Wiener's Construction
8.1.2.3.
Lévy's Construction
8.2.
Basic Properties
8.2.1.
Continuous Sample Paths
8.2.2.
Stationary Increments
8.2.3.
Independent Increments
8.2.4.
Gaussian Increments
8.2.5.
Initial Condition
8.3.
Scaling and Self-Similarity
8.3.1.
Scaling Property
8.3.2.
Self-Similarity
8.3.3.
Fractal Dimension
8.4.
Sample Path Properties
8.4.1.
Continuity
8.4.2.
Non-Differentiability
8.4.3.
Hölder Continuity
8.4.4.
Quadratic Variation
8.4.5.
Law of Iterated Logarithm
8.5.
Martingale Properties
8.5.1.
Brownian Motion as Martingale
8.5.2.
Quadratic Variation Process
8.5.3.
Exponential Martingales
8.6.
Maximum and Minimum Processes
8.6.1.
Running Maximum
8.6.2.
Reflection Principle
8.6.3.
Distribution of Maximum
8.6.4.
Joint Distributions
8.7.
First Passage Times
8.7.1.
Hitting Times for Levels
8.7.2.
Distribution of First Passage Times
8.7.3.
Inverse Gaussian Distribution
8.8.
Variations of Brownian Motion
8.8.1.
Brownian Motion with Drift
8.8.2.
Geometric Brownian Motion
8.8.3.
Brownian Bridge
8.8.4.
Fractional Brownian Motion
8.9.
Multidimensional Brownian Motion
8.9.1.
Definition and Properties
8.9.2.
Independence of Components
8.9.3.
Rotational Invariance
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9. Stochastic Calculus