- #1
Dragonfall
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This came up a while ago in a post. What is a sensible way of defining a "random" curve in R^n? Let's say n=2 in order to keep things simple.
Dragonfall said:Then I'm guessing we can only have finitely many random points. I don't think all curves can be described this way.
Who do you mean by "we"? I know how to define "random": according to some probability distribution. That's why I asked what probability distribution you wanted to use.Dragonfall said:Curve = continuous map from a real interval to R^n
Random is what we're trying to define.
HallsofIvy said:Who do you mean by "we"? I know how to define "random": according to some probability distribution. That's why I asked what probability distribution you wanted to use.
AUMathTutor said:I guess this is what you mean by order n=2
Dragonfall said:This came up a while ago in a post. What is a sensible way of defining a "random" curve in R^n? Let's say n=2 in order to keep things simple.
trambolin said:random points + bezier curves on computer?
gel said:The question is rather ill-defined. There's lots of ways of generating random curves. The method you choose depends on what properties you want. A standard one is the http://en.wikipedia.org/wiki/Wiener_process" (aka Brownian motion), which is nowhere differentiable.
ice109 said:does that generate a c1 curve? what stochastic process does?
A random curve in R^n is a mathematical concept that describes the path of a point moving randomly in n-dimensional space. It is typically represented by a continuous function that maps a real number (time) to a point in n-dimensional space.
A deterministic curve is a curve that follows a specific, predetermined pattern or equation. In contrast, a random curve does not follow a specific pattern and is instead determined by a random process.
In the context of random curves in R^n, randomness is defined as the lack of a predictable pattern or regularity in the curve's path. This means that the curve's behavior cannot be determined or predicted with certainty.
Random curves in R^n have various applications in fields such as physics, biology, finance, and computer graphics. They can be used to model the movement of particles in a gas, the growth of a population, stock market fluctuations, and the animation of natural-looking movements in computer-generated imagery.
Some common techniques used to define random curves in R^n include stochastic processes, Markov chains, and Brownian motion. These techniques involve using probability and statistics to describe the random behavior of the curve and make predictions about its future path.