Talk:Stochastic differential equation

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Created a new page here from what was previously all in Langevin equation - which is actually more specific than just an SDE. More work is needed. --SgtThroat 15:04, 4 Jan 2005 (UTC)

I don't get it: "A weak solution consists of a probability space and a process that satisfies the integral equation, while a strong solution is a process that satisfies the equation and is defined on a given probability space."

I read this to say that a weak solution satisfies A&B, while a strong solution satisfies B&A. Please clarify.

Terminology?[edit]

The terminology subsection currently says that SDEs can be written in one of three forms, the third of which is "as an SDE". Could someone clarify that?

Also, under "use in physics", it says that the noise is multiplicative if g_i(x) is not constant. Isn't it only multiplicative if g_i(x)\propto x? LachlanA (talk) 05:29, 15 January 2008 (UTC)

weak vs strong solution, a typo[edit]

I think the formulation in the text is correct and slightly different from what you quoted, namely ``... strong solution ... in the given probability measure". Where ``the" refers to the given probability measure of the SDE. This requires especially that the solution process X_t is defined for all elementary events ω. While for a weak solution you can first restrict the probability measure (and especially its underlying sigma-algebra) to a convenient subspace (possibly rescaling the prob-measure).

Towards the end of the current article I guess there is a typo in the formulation of the Ito-SDE. Namely you should not set equal the process X_t (for all t) to the random variable Z. It would make sense if you wrote X_0=Z. Before I change this I'd prefer someone had a look in one of the literature sources and check if that is correct.

[melli]64.178.100.37 (talk) 04:10, 16 March 2008 (UTC)

History of SDE[edit]

This is the first time I am making a posting. I may not be doing this the best way possible.

The article correctly cites Bachelier (1900) as one who wrote the stochatic DE before Enstein.

It turns out that prior to Bachelier, Francis Ysidro Edgeworth (1883) and Lord Rayleigh (1880, 1894)


Edgeworth, F.Y., The law of error, Phil. Mag., Fifth ser., 16, 300-309, 1883.

Rayleigh, Lord, On the resultant of a large number of vibrations of the same pitch and of arbitrary phase, Phil. Mag., 10, 73-78, 1880.

Rayleigh, Lord, The Theory of Sound, MacMillan and Co., London, Vol. 1, Second Edition, 1894.

Historical details can be found in,

Narasimhan, T. N., Fourier's Heat Conduction Equation: History, Influence, And Connections, Reviews of Geophysics, 37(1), 151-172, 1999


Narasimhan —Preceding unsigned comment added by Tnnarasimhan (talkcontribs) 23:30, 3 May 2008 (UTC)

Use in probability and financial mathematics[edit]

Here:

This equation should be interpreted as an informal way of expressing the corresponding integral equation
 X_{t+s} - X_{t} = \int_t^{t+s} \mu(X_u,u) \mathrm{d} u + \int_t^{t+s} \sigma(X_u,u)\, \mathrm{d} B_u .
The equation above characterizes the behavior of the continuous time stochastic process Xt as the sum of an ordinary Lebesgue integral and an Itō integral.

I think the enhanced adjective is misleading: there's nothing ordinary in the integration of an expression that includes a stochastic process! For example, what is the ordinary integral of:

\int_{t_1}^{t_2} B_t \ \mathrm{d} t\, ?

Albmont (talk) 18:30, 29 July 2009 (UTC)

You can regard \int_{t_1}^{t_2} B_t(\omega) \ \mathrm{d} t\, pathwise as an ordinary Lebesgue integral (or, since Brownian paths are almost surely continuous, as a Riemann integral for almost every fixed \omega 129.177.32.74 (talk) 09:54, 2 April 2014 (UTC) Georgy

Use in physics[edit]

The overall article is already very technical. Why not have a juicy example here instead of the general form of the equation? I pasted a suggestion below.

More remarks: - does the chain rule for nonlinear SDEs with additive noise? I doubt it. - There are two statements about misleading terminology (THE Langevin ..., MULTIPLICATIVE noise): lets put less emphasis

Application in physical sciences: The OUP is a prototype of a noisy relaxation process. Consider for example a Hookean spring with spring constant k whose dynamics is highly overdamped with friction coefficient \gamma. In the presence of thermal fluctuations with temperature T, the length x(t) of the spring will fluctuate stochastically around the spring rest length x_0; its stochastic dynamic is described by an OUP with \theta=k/\gamma, \mu=x_0, \sigma=\sqrt{2k_B T/\gamma}. (The equation for the effective diffusion constant D=\sigma^2 is the famous Einstein relation.) In physical sciences, the stochastic differential equation of an OUP is rewritten as a Langevin equation

 \gamma\dot{x} = - k( x - x_0 ) + \xi

where \xi(t) is Gaussian white noise with <\xi(t_1)\xi(t_2)>= 2 k_B T \delta(t_1-t_2). If temperature is a function of position T=T(x), the noise term is said to be multiplicative and care has to be taken in manipulating the equation to avoid the Ito-Stratonovich dilemma.