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Stochastic Learning StudioBeta
1.1Symmetric Random Walk1.2Rescaled Random Walk1.3Standard deviation1.4Central Limit Theorem2.1Defining the Wiener process2.2Increment explorer2.3Joint distributions and covariance3.1Running maximum and minimum3.2Reflection principle3.3First passage times3.4Hitting probabilities
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Curriculum/Chapter 2 · Brownian Motion Fundamentals/2.1
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2.1

Defining the Wiener process

Which properties turn a random process into Brownian motion?

Goal: see each of the four defining properties in an experiment — and understand why paths are continuous yet nowhere differentiable.

Simulation controls

×1
014
Vertical axis
7
19999

Measurements

Window ε
1.00
sd W(ε) = √ε
1.00

Try this

  1. Zoom in with the fixed axis: where does the cloud go as ε → 0?
  2. Switch to the scaled axis and keep zooming: why does the picture not change?
Zooming in on t = 0
window [0, 1.00] · sd W(ε) = √ε = 1.00

On a fixed axis the cloud shrinks into zero: W₀ = 0 and the path starts without a jump. On the scaled axis it looks the same at every zoom — that is Brownian scaling.