Talk:Lévy flight

From Wikipedia, the free encyclopedia

Is Lévy name of a person? Jay 04:19, 24 Mar 2004 (UTC)

Anyone would kindly illustrate the difference and relationship between Lévy flight and Power law? Thanks.

Seems clear to me: a "power law" is a probability distribution; a Lévy flight is a stochastic process, having a probability distribution at each point in time. Doesn't the article make that clear? Michael Hardy 22:10, 15 Mar 2005 (UTC)

There are certainly some inaccuracies in this article, unfortunately I dont know enough about Levy flights to correct them. For example take the paragraph:

"According to the central limit theorem, if the distribution were to have a finite variance, then after a large number of steps, the distance from the origin of the random walk would tend to a normal distribution. (This type of random walk is also known as Brownian motion)."

1. The distance (euclidian I suppose) from the origin of a stochastic process is always positive, which means it can not be normally distributed (except for the trivial/pathological case that the process stays at the origin forever). Maybe what is meant is that the projections of the process on its coordinates are normally distributed (or in other words the process is asymptotically Gaussian)

2. Normal distribution alone is not sufficient for a stochastic Process to make it a Brownian Motion.

I agree. In a Brownian motion, the increments even over short time intervals are normally distributed; the central limit theorem would apply only to sufficiently long intervals. Michael Hardy 22:00, 14 Jun 2005 (UTC)

How about this: "According to the central limit theorem, if the distribution were to have a finite variance, then after a large number of steps, the distance from the origin of the random walk would tend to a xxx distribution. (This type of random walk is also known as Brownian motion)."

I don't know what xxx should be. Its a bivariate normal distribution but with independent components. PAR 04:46, 15 Jun 2005 (UTC)

Yes, with the distance it is meant the projection to the y-axis. It must also be included that it has to be properly normalized. This convergence is Donskers Invariance Theorem. An advanced result in probability theory, which gives weak convergence in the Skorokhod-topology for a normalized sum to a Brownian-motion process. However, there does not exist any wikipedia-articles on any of these subjects. Billingsley's "Convergence of Probability Measures" is the classic in this field for those who want to check it out. --Steffen Grønneberg 19:17, 15 May 2006 (UTC)

[edit] Too technical

Someone who can translate this article into layman's terms ought to have a crack at revising this article. —thames 18:42, 6 December 2005 (UTC)

To help someone achieve that, can you explain which parts of the article you found difficult to understand ? Have you read the related articles on random walks, probability distributions and the Lévy distribution ? Do these help you understand the Lévy flight article ? Gandalf61 09:17, 7 December 2005 (UTC)

No response to the above questions after waiting for a week, so I have removed the technical tag. Gandalf61 15:50, 14 December 2005 (UTC)

[edit] Biased toward axes?

It seems that the Lévy flight given in the example is biased toward going large distances along either the x-axis or the y-axis. Would it not be more accurate to have each step of the Lévy flight going in a randomly selected direction? --Zemyla^t 16:55, 25 May 2006 (UTC)

I agree. The biasing is caused by choosing the x and y increments independently, as stated in the figure caption. Large steps are rare, and it is even rarer that they occur simultaneously both along x and y. Thus polar coordinates should be used instead, with a random uniform distributed angle, and a random Lévy distributed modulus. GuidoGer 11:55, 19 September 2006 (UTC)