About stochastic process....Help please

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In the discussion about Gaussian and Poisson processes, it is clarified that transformations like X(2t) still satisfy the definition of a Gaussian process, although their mean and variance differ from those of X(t). The original solutions incorrectly stated that X(2t) is not a Gaussian or Poisson process, which is seen as a misunderstanding of the fundamental properties of these processes. The importance of scaling properties in stochastic processes is emphasized, highlighting their relevance in modeling and applications. The conversation critiques the potential misleading nature of the definitions used in educational materials. Overall, the correct interpretation of these processes is crucial for accurate understanding and application in stochastic modeling.
hojoon yang
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Given a Gaussian process X(t), identify which of the following , if any, are gaussian processes.

(a)X(2t)

solution said that X(2t) is not gaussian process, since

upload_2015-6-17_0-58-42.png


and similarly

Given Poisson process X(t)

(a) X(2t)

soultion said that X(2t) is not poisson process, since same reason above.

upload_2015-6-17_1-1-21.png


BUT

I think that in stochastic process, Time t is just constant value.

so I think X(2t), X(10000t), X(t+100) is also gaussian process ,or poisson process

doesn't care about whatever t is.

answer is?
 
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hojoon yang said:
Given a Gaussian process X(t), identify which of the following , if any, are gaussian processes.

(a)X(2t)

solution said that X(2t) is not gaussian process, since

View attachment 84875

and similarly

Given Poisson process X(t)

(a) X(2t)

soultion said that X(2t) is not poisson process, since same reason above.

View attachment 84876

BUT

I think that in stochastic process, Time t is just constant value.

so I think X(2t), X(10000t), X(t+100) is also gaussian process ,or poisson process

doesn't care about whatever t is.

answer is?

You are correct; if one looks at the usual definition of a Gaussian process, Y(t) =X(2t) satisfies the definition. However, its ##\mu## and ##\sigma## are different from those of X(t). Maybe your book uses some really weird definition of Gaussian process, but I hope not---as that would be misleading generations of students. See, eg., https://en.wikipedia.org/wiki/Gaussian_process . The same remarks apply to your Poisson process case.

Frankly, I am surprised someone would make those types of errors, because the scaling properties (of Poisson processes, in particular) are absolutely fundamental in modelling and applications.
 
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Ray Vickson said:
You are correct; if one looks at the usual definition of a Gaussian process, Y(t) =X(2t) satisfies the definition. However, its ##\mu## and ##\sigma## are different from those of X(t). Maybe your book uses some really weird definition of Gaussian process, but I hope not---as that would be misleading generations of students. See, eg., https://en.wikipedia.org/wiki/Gaussian_process . The same remarks apply to your Poisson process case.

Frankly, I am surprised someone would make those types of errors, because the scaling properties (of Poisson processes, in particular) are absolutely fundamental in modelling and applications.

Thanks for reply vickson!
 
I picked up this problem from the Schaum's series book titled "College Mathematics" by Ayres/Schmidt. It is a solved problem in the book. But what surprised me was that the solution to this problem was given in one line without any explanation. I could, therefore, not understand how the given one-line solution was reached. The one-line solution in the book says: The equation is ##x \cos{\omega} +y \sin{\omega} - 5 = 0##, ##\omega## being the parameter. From my side, the only thing I could...

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