What is the Role of Epsilon in Stochastic Continuity?

In summary, ε is an arbitrary small number that is used as an upper bound for the magnitude difference of two random variables. It is not related to any axis and should be understood as just a number, rather than a point on a specific axis.
  • #1
woundedtiger4
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  • #2
ε is an arbitrary (small) number > 0.
If you are hung up on using axes, s and t are points on the x axis. Xt is a point on the y axis, but it is a random variable rather than just a number.
 
  • #3
Then, is epsilon on y-axis?
 
  • #4
woundedtiger4 said:
Then, is epsilon on y-axis?

You are confusing intuitive visualizations of mathematics with the content of mathematical definitions. Even in calculus, there is nothing in the definition of limit that says that epsilon in "on the y-axis".
 
  • #7
I know what you mean actually I can't understand without visualising therefore it is irritating me that what is epsilon intuitively in continuity of stochastic process? I know the op is about jump discontinuity which is RCLL function so is the epsilon shows any point between the jump ?
 
  • #9
woundedtiger4 said:
Then, is epsilon on y-axis?

ε is a positive number. It is used as an upper bound of the magnitude difference of two random variables. There is no axis involved. If you insist on thinking "axis", then you may consider everything on the y axis. However, I suggest you try to understand the main point, there is no axis involved, just numbers.
 
  • #10
No, I wasn't thinking epsilon on y-axis,I just tried to give an example that like in calculus I used to think epsilon (not the same epsilon shown in stochastic continuity) as on y-axis for which the delta exists. I didn't mean that the epsilon in stochastic continuity is on y-axis .
Thanks a tonne because now I have understood it.
 

Related to What is the Role of Epsilon in Stochastic Continuity?

1. What is a continuous stochastic process?

A continuous stochastic process is a mathematical model used to describe the evolution of a system over time, where the behavior of the system is influenced by random or unpredictable events. It is continuous because it is defined for all points in time, and stochastic because it is based on probabilistic outcomes.

2. What are the key characteristics of a continuous stochastic process?

The key characteristics of a continuous stochastic process include being continuous in time, having random or unpredictable events that affect the system, and being described by probability distributions. It also has a state space, which is the set of all possible values the system can take on, and a transition function, which determines how the system evolves over time.

3. What is the difference between a discrete and continuous stochastic process?

The main difference between a discrete and continuous stochastic process is the time interval at which the process is observed and the values it can take on. A discrete process is observed at specific time intervals and can only take on discrete values, while a continuous process is observed at all points in time and can take on any value within a given range.

4. How is a continuous stochastic process used in real-world applications?

A continuous stochastic process is used in a variety of fields, including finance, engineering, physics, and biology. It is used to model and predict the behavior of complex systems, such as stock prices, weather patterns, and biological processes. It can also be used to simulate and analyze the performance of systems in different scenarios.

5. What are some common examples of continuous stochastic processes?

Some common examples of continuous stochastic processes include Brownian motion, which describes the random movement of particles in a fluid, and the Ornstein-Uhlenbeck process, which models the behavior of a damped harmonic oscillator. Other examples include the geometric Brownian motion used in finance and the Poisson process used in queuing theory.

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