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Fractional Brownian Motion

Probability and Statistics

Fractional Brownian motion is a generalization of ordinary Brownian motion in which the increments of the process are no longer required to be independent of one another. It is formally defined as a continuous-time Gaussian process that starts at zero, has an expected value of zero at every time, and has a covariance function built around a parameter H, called the Hurst parameter, which ranges between 0 and 1 and controls how rough or smooth the resulting path looks: higher values of H produce smoother trajectories. Setting H to exactly one half recovers standard Brownian motion, while values above one half produce positively correlated increments and values below one half produce negatively correlated ones. The process was introduced by Benoit Mandelbrot and John van Ness in 1968 and has stationary increments known as fractional Gaussian noise. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/

Facts
Classification
Object Kind
Mathematical Model 1
Origin Year
1968 1
introduced by Mandelbrot and van Ness (1968)
Connections

In Branch

Source Fractional Brownian Motion (Wikipedia)

Is Kind Of Object

Entity-backed identity for the object-kind enum value this mathematical object already carries, resolved to a mathematics concept by an explicit value-to-entity map (phase 3 bucket conversion, docs\design_entity_backed_browse_buckets_20260928.md). The object-kind fact itself stays on the object unchanged.

Sources
1. Fractional Brownian Motion (Wikipedia)
In Branch: Probability and Statistics, Lead sentence
Quote, In Branch: Probability and Statistics, Lead sentence
In probability theory, fractional Brownian motion (fBm), also called a fractal Brownian motion, is a generalization of Brownian mo
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