Rotational Brownian motion (astronomy) that the rotation axis of the binary is oriented at angle θ at time "t". The evolution equation for "F" is formula_17 If <Δξ>, "a", ρ and σ are constant in time, this becomes formula_18 where μ = cos θ and τ is the time in units of the relaxation time "t", where formula_19 The solution to this equation states that the expectation value of μ decays with time as formula_20 Hence, "t" is the time constant for the binary's orientation to be randomized by torques from field stars. Rotational Brownian motion was first discussed in the context of binary
Geometric Brownian motion above: formula_27. import numpy as np import pandas as pd import matplotlib.pyplot as plt mu=1 n=50 dt=0.1 x0=100 x=pd.DataFrame() np.random.seed(1) for sigma in np.arange(0.8,2,0.2): x.columns=np.arange(0.8,2,0.2) plt.plot(x) plt.legend(x.columns) plt.xlabel('t') plt.ylabel('X') plt.title('Realizations of Geometric Brownian Motion with different variances\n mu=1') plt.show() GBM can be extended to the case where there are multiple correlated price paths. Each price path follows the underlying process where the Wiener processes are correlated such that formula_31 where formula_32. For the multivariate case, this implies that Geometric Brownian motion is used to model stock prices in the Black–Scholes model and is the most widely used model of stock