Help with stochastic processes

In summary, a stochastic process involves predictable actions and a random element, which can compensate for unknown or non-predictable actions. The extent of this randomness depends on the underlying event space of the process.
  • #1
FlufferNuterFSU
17
0
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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  • #2
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.
 

1. What are stochastic processes?

Stochastic processes are a mathematical model used to describe the random evolution of a system over time. They are used in various fields, such as finance, physics, and biology, to model uncertain or unpredictable events.

2. What are some examples of stochastic processes?

Some examples of stochastic processes include stock market fluctuations, weather patterns, radioactive decay, and genetic mutations.

3. How are stochastic processes used in scientific research?

Stochastic processes are used in scientific research to model and analyze complex systems that involve randomness or uncertainty. They can help scientists make predictions and understand the behavior of natural phenomena.

4. What is the difference between a stochastic process and a deterministic process?

A deterministic process is one where the outcome is completely determined by the initial conditions and there is no randomness involved. In contrast, a stochastic process involves randomness and the outcome is not completely predictable.

5. What are some common applications of stochastic processes?

Stochastic processes have many applications, including financial modeling, risk analysis, population dynamics, and signal processing. They are also used in engineering for quality control and in computer science for simulation and optimization.

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