Problem related to the compound Poisson process (?)

In summary, the speaker is seeking help in finding the expected number of events in a continuous time process with alternating exponentially distributed events. They have attempted to solve it themselves but are looking for guidance and suggestions from others. The proper terminology for this type of model is a "continuous time Markov process." The speaker will continue to research and share any useful results they find.
  • #1
gabe_rosser
2
0
Dear all,

I wonder if anyone has come across this problem before and could point me to a relevant ref or tell me what terms I might search for:

I am interested in a continuous time process in which two alternating events (call them A and B) occur. Each event has an exponentially distributed duration, with different rate constants. They occur consecutively and exclusively: A immediately follows B and vice-versa, with no overlapping or gaps.

Our experiment always starts with A. After an exponentially distributed waiting time, B occurs. After a second time wait, exponentially distributed but with different rate constant, A occurs again, etc.

I am seeking the expected number of events (or pairs of events) that occur in a given time interval.

I have attempted this myself, but my approach became complicated quite rapidly so I thought I would check here first to see if anyone had come across this before.

Thanks, and apologies for the lengthy description.

Gabriel
 
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  • #2
I think the proper terminology for your model is a "continuous time Markov process". Look it up and see if that fits. If not, let us know.
 
  • #3
Thanks for the suggestion. I should have thought to think of it as a Markov process.

I'm still not sure how to use this description to get at my desired result, however. Does anyone know of any results relating to the expected number of transition events for a given Markov chain? I'll look into this as well and post if I find anything useful.
 

1. What is a compound Poisson process?

A compound Poisson process is a type of stochastic process used in probability theory. It is a combination of a Poisson process, which is a counting process, and a random variable, which represents the size of each event. The compound Poisson process is often used to model phenomena that involve random events occurring at random times.

2. What are some real-world applications of the compound Poisson process?

The compound Poisson process has a wide range of applications in various fields such as finance, insurance, and telecommunications. It can be used to model stock prices, insurance claims, and the arrival of phone calls or internet traffic, among others.

3. How is the compound Poisson process different from a regular Poisson process?

The main difference between a compound Poisson process and a regular Poisson process is that the compound process takes into account the variability of the size of each event, while the regular process assumes a fixed event size. This makes the compound process more versatile and applicable to a wider range of real-world scenarios.

4. What are the key parameters of a compound Poisson process?

The key parameters of a compound Poisson process are the rate parameter, which represents the average rate of events occurring, and the distribution of the random variable, which represents the size of each event. These parameters can be adjusted to fit different scenarios and to study the behavior of the process over time.

5. Can the compound Poisson process be used for forecasting?

Yes, the compound Poisson process can be used for forecasting future events based on historical data. By estimating the parameters of the process and using statistical techniques, we can make predictions about the future behavior of the process. However, like any model, the accuracy of the forecast depends on the quality of the data and the assumptions made about the process.

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