Brownian Motion is a stochastic process W(t) which has the following four properties:
1] W(0) = 0,
2] almost surely, the trajectory of W(t) is continuous;
3] W(t) has independent increments: for any moments of time s < t < u, random variables W(t) - W(s) and W(u) - W(s) are independent;
4] W(t) has stationary increments: for any moments of time s < t and positive shift h, random variables W(s+h) - W(s) and W(t+h) - W(t) have the same distribution.
It follows from properties 3, 4 and the Central Limit Theorem that any finite-dimensional distributions of a Brownian motion are Gaussian (normal). For example, in the definition above, random variables W(t) - W(s), W(u) - W(s), W(s+h) - W(s) and W(t+h) - W(t) are jointly Gaussian.
Brownian motion is also known as "standard Wiener process".
Sunday, November 13, 2016
Subscribe to:
Post Comments (Atom)
Blog Archive
-
▼
2016
(87)
-
▼
November
(26)
- Image Recognition in Python
- Basic Github
- IPython and Using Notebooks
- WGSN Insight
- Cold Start Analysis
- Optimal Frequency
- Google Analytics Reports - Key KPIs
- Use Cases by Artificial Intelligence
- User Journey
- Store Attribute Model
- Product Affinity Segmentation
- Marketing Mixed Modeling
- Things must to remember
- Check Bivariate Distribution in R
- Check Univariate Distribution in R
- Grep Strs in R
- Clarifai Python API in Python
- Metadata Management
- Jump Diffusion
- Brownian Motion
- Common Unix Command
- ToDate Format in Pig
- Read Json in Pig
- Python UDF in Pig
- Machine Learning Models Summary
- 2 - Scale Google Trend Data
-
▼
November
(26)
No comments:
Post a Comment