Brownian Motions

Brownian motion describes the stochastic diffusion of particles as they travel through n-dimensional spaces filled with other particles and physical barriers. Here the term particle is a generic term that can be generalized to describe the motion of molecule (e.g. H₂O) or proteins (e.g. NMDA receptors); note however that stochastic diffusion can also apply to things like the price index of a stock (see random walk) or the propagation heat energy across a surface. Brownian motion is among the simplest continuous-time stochastic processes, and a limit of various probabilistic processes (see random walk). As such, Brownian motion is highly generalizable to many applications, and is directly related to the universality of the normal distribution. In some sense, stochastic diffusion is a pure actuation of the basic statistical properties of probability distributions - it is distribution sampling translated into movements.

SPECIFICATION - Physical experiments suggest that Brownian motion has

• continuous increments
• increments of a particle over disjoint time intervals are independent events
• each increment is assumed to result from collisions with many molecules
• each increment is assumed to have a normal probability distribution
  • (Central Limit Theorem of probability theory: the sum of a large number of independent identically distributed random variables is approximately normal)
• the mean increment is zero as there is no preferred direction
• the position of a particle spreads out with time
• the variance of the increment is proportional to the length of time that Brownian motion has been observed

The probability density of a normally distributed random variable with mean μ and standard deviation σ is given by:

$f(x,\mu ,\sigma )={\frac {1}{\sigma {\sqrt {2\pi }}}}e^{-{\frac {1}{2}}({\frac {x-\mu }{\sigma }})^{2}}$

Mathematically, the random process called Brownian motion is denoted here as B(t) and defined for times t ≥ 0; the probability density of Brownian particles at the end of time period [0, t] is obtained by substituting μ = 0 and σ = √t, giving:

${\frac {1}{\sqrt {t2\pi }}}e^{-{\frac {1}{2}}({\frac {x}{\sqrt {t}}})^{2}}$

where x denotes the value of random variable B(t). The probability distribution of the increment B(t + u) − B(t) is:

$P[B(t+u)-B(t)\leq a]=\int _{x=-\infty }^{a}{\frac {1}{\sqrt {u2\pi }}}e^{-{\frac {1}{2}}({\frac {x}{\sqrt {u}}})^{2}}dx$