Example of stochastic differential equations

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SUMMARY

The discussion centers on solving a stochastic differential equation (SDE) of the form dX/dt = aX² + bX + c + sXn(t), where n(t) represents white noise with a mean of 0 and a variance of 1. The user seeks a complete solution to this equation, noting that the absence of the quadratic term would simplify the problem. Additionally, it is highlighted that the stationary probability density function (pdf) of X corresponds to the inverse Gaussian distribution.

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  • Understanding of stochastic differential equations
  • Familiarity with white noise processes
  • Knowledge of inverse Gaussian distribution
  • Basic calculus and differential equations
NEXT STEPS
  • Research methods for solving nonlinear stochastic differential equations
  • Explore the properties of the inverse Gaussian distribution
  • Learn about numerical simulation techniques for SDEs
  • Investigate the Itô calculus framework for stochastic processes
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Mathematicians, statisticians, and researchers in fields involving stochastic processes, particularly those focused on solving complex stochastic differential equations.

hkour
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hello to everyone,

I have a problem solving a stochastic differential equation of the form:

dX/dt=aX²+bX+c+sXn(t),

where n(t) is white noise with a mean value equal to 0 and variance equal to one.

Does anyone know the solution of this stochastic differential equation or how to solve it?

Thank you
 
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Without that quadratic term, this would be easy, but...

Where does this come from? What exactly do you need? Do you need a full solution, or would it be good enough to predict the mean of X?
 
the stationary pdf of X gives the inverse gaussian distribution.
I need the full solution
 

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