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Brownian motion |
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Brownian motion[[Image:BrownianMotion.png|thumb|right|256px|An example of 1000 simulated steps of Brownian motion in two dimensions. The origin of the motion is at [0,0] and the x and y components of each step are independently and normally distributed with variance 2 and mean 0. The mathematical model posits motion in which the steps are not discrete.The term Brownian motion (in honor of the botanist Robert Brown) refers to either The mathematical model can also be used to describe many phenomena not resembling (other than mathematically) the random movement of minute particles. An often quoted example is stock market fluctuations. Another example is the evolution of physical characteristics in the fossil record. Brownian motion is among the simplest stochastic processes on a continuous domain, and it is a limit of both simpler (see random walk) and more complicated stochastic processes. This universality is closely related to the universality of the normal distribution. In both cases, it is often mathematical convenience rather than accuracy as models that motivates their use. All three quoted examples of Brownian motion are cases of this:
Description of the mathematical modelMathematically, Brownian motion is a Wiener process in which the conditional probability distribution of the particle's position at time t+dt, given that its position at time t is p, is a normal distribution with a mean of p+μ dt and a variance of σ2 dt; the parameter μ is the drift velocity, and the parameter σ2 is the power of the noise. These properties clearly establish that Brownian motion is Markovian (i.e. it satisfies the Markov property). Brownian motion is related to the random walk problem and it is generic in the sense that many different stochastic processes reduce to Brownian motion in suitable limits. In fact, the Wiener process is the only time-homogeneous stochastic process with independent increments that has continuous trajectories. These are all reasonable approximations to the physical properties of Brownian motion. The mathematical theory of Brownian motion has been applied in contexts ranging far beyond the movement of particles in fluids. For example, in the modern theory of option pricing, asset classes are sometimes modeled as if they move according to a closely related process, geometric Brownian motion. It turns out that the Wiener process is not a physically realistic model of the motion of Brownian particles. More sophisticated formulations of the problem have led to the mathematical theory of diffusion processes. The accompanying equation of motion is called the Langevin equation or the Fokker-Planck equation depending on whether it is formulated in terms of random trajectories or probability densities. See alsoReferences
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