What is a Stochastic Integral and How Does it Differ from a Regular Integral?

AI Thread Summary
A stochastic integral is defined as W(t) = ∫₀ᵗ f(ξ)dX(ξ), resembling a Riemann sum, but incorporates randomness through the Wiener process. It differs from a regular integral by incorporating statistical elements, such as averages and standard deviations, in its calculations. The relationship dW = f(ξ)dX is also discussed, indicating a connection between the stochastic integral and differential forms. The conversation highlights the importance of Norbert Wiener in understanding these concepts. Overall, stochastic integrals are fundamentally tied to randomness and statistical analysis.
courtrigrad
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Hello all

Let's say we define a stochastic integral as:
W(t) = \int^{t}_{0} f(\varsigma)dX(\varsigma) = \lim_{n\rightarrow\infty} \sum^{n}_{j=1} f(t_{j-1})(X(t{j})) - X(t_{j-1})) with t_{j} = \frac{jt}{n} IS this basically the same definition as a regular integral?

Also if we have W(t) = \int^{t}_{0} f(\varsigma) dX(\varsigma) then does dW = f(\varsigma) dX?

Thanks
 
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In the first integral i can see a strong resemblence with the Riemann sum...As for the second (and for the first too),who's zeta...?

Daniel.
 
The Weiner process the one you are looking for and luckily old Norbert worked it out for us. This really becomes more statistical than anything because we have to talk about the average or standard deviation of each step in the integral. It has been a little while and I don't have any notes with me at the present moment but Norbert is the man to look into to wrap your mind around stochastic integrations!
 
zeta is a variable corresponding to time
 
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