Is the Random Element in Stochastic Processes for Compensating Unknown Actions?

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SUMMARY

The discussion centers on the role of the "random element" in stochastic processes, specifically regarding its function in compensating for unknown actions. A stochastic process is defined as a set of random variables indexed by an ordered set, where predictable actions are derived from the dependency structure of the event space. The random element serves to account for uncertainties not captured by these predictable actions, as illustrated by the example of a coin toss, where the underlying event space can vary in complexity. This highlights the importance of understanding both predictable and random components in modeling stochastic processes.

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FlufferNuterFSU
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From my extremely small and inadequate knowledge of stochastic processes (and Wikipedia):

A stochastic process is a process in which some later state is determined by predictable actions and by a random element.

Now the question: this "random element" is this meant to compensate for unknown (non-predictable) actions or is this just a random factor for some other purpose? Hope that makes sense. Thanks.
 
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FlufferNuterFSU said:
A stochastic process is a process in which some later state is determined by predictable actions and by a random element.

In a way, yes. The "actions" are implicit in the underlying event space and dependency structures. In general definition of a stochastic process as a set of random variables indexed by an ordered set, the predictable part would come from our knowledge of the dependency structure and the particular sample path; the random part would be everything else we don't know about the sample path.

FlufferNuterFSU said:
this "random element" is this meant to compensate for unknown (non-predictable) actions or is this just a random factor for some other purpose?

Yes - it depends on how the underlying event space is formulated. Take a coin toss as an example, where [tex]X_t=X_0[/tex] for t>0 and [tex]X_0[/tex] is a random variable that maps events to either 0 or 1. The underlying event space could be just the 2 events Heads or Tails, or it could be a description of the entire universe leading up to the point in time of the coin toss. The former model summarizes the uncertainties of the latter.

Hope this helps.
 

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