Calculating Poisson Process probabilities

In summary: Overall, your reasoning is solid but you need to adjust your calculations slightly. Keep up the good work!In summary, the conversation is about determining probabilities in a Poisson process, where intervals may or may not be independent. The speaker has solved some problems using their own assumptions and calculations, but is unsure if their methods are correct. They are seeking confirmation and clarification on their approach. The expert advises them to consider conditional probability and the law of total probability in their calculations.
  • #1
shan
57
0
I just want to check my answers/reasoning as I'm not sure if I assumed the right things to do these problems.

N = {N(t), t>=0} ~ Poisson(1) and [tex]N_{(t,t+h]} = N(t+h)-N(t)[/tex]

Determine P(N(4) =3|N(2) = 1)
Here I presumed that since N(2) = 1, then there must be 2 more arrivals in the interval (2,4] so that N(4)=3 so I calculated
[tex]P(N_{(0,2]} = 1 and N_{(2,4]} = 2) = P(N(2) = 1)P(N(2) = 2) = 4e^{-4}[/tex]
since non-overlapping intervals are independent so I can multiply the probabilities (?). However I'm not too sure if I can break the intervals up like that?

Determine [tex]P(N_{(4,7]} = 2 and N_{(3,6]} = 1)[/tex]
Here I split up the intervals into (3,4], (4,6] and (6,7] although like I said before, I'm not sure if that should be done. Then I looked for the different combinations of events over those intervals so that the above would be true ie
[tex]P(N_{(3,4]} = 0 and N_{(4,6]} = 1 and N_{(6,7]} = 1) + P(N_{(3,4]} = 1 and N_{(4,6]} = 0 and N_{(6,7]} = 2) = \frac{5e^{-4}}{2}[/tex]
Again I assumed non-overlapping intervals were independent so I could multiply the probabilities together. It would be great if someone could double-check for me that those two are the only combinations (I did it three times already but I'm paranoid).

Determine [tex]P(N_{(4,7]} = 2|N_{(1,5]} = 2)[/tex]
I used the same sort of steps as I did before although it's a conditional but I thought that if I split up the intervals and made it independent, conditionals would not matter. I used intervals (1,4], (4,5] and (5,6] and found the combinations
[tex]P(N_{(1,4]} = 2 and N_{(4,5]} = 0 and N_{(5,7]} = 2) + P(N_{(1,4]} = 1 and N_{(4,5]} = 1 and N_{(5,7]} = 1) + P(N_{(1,4]} = 0 and N_{(4,5]} = 2 and N_{(5,7]} = 0) = \frac{31e^{-6}}{2}[/tex]
 
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  • #2
Hi there! It seems like you have a good understanding of the Poisson process and your approach to the questions is generally sound. However, for the second and third questions you need to consider the fact that the intervals are not independent, since they overlap in some parts. Therefore, you can't simply multiply the probabilities together as you have done. I recommend looking into conditional probability and the law of total probability to help find the correct answer.
 

What is a Poisson process?

A Poisson process is a mathematical model that describes the occurrence of events over a fixed interval of time. It is a type of counting process where the number of events that occur in a given time period follows a Poisson distribution.

How do you calculate the probability of a certain number of events occurring in a Poisson process?

The probability of a certain number of events occurring in a Poisson process can be calculated using the Poisson distribution formula: P(X = k) = (e^-λ * λ^k) / k!, where λ is the average number of events per unit time and k is the number of events we are interested in.

What is the difference between a Poisson process and a binomial distribution?

A Poisson process is a continuous model for counting events over a fixed interval of time, while a binomial distribution is a discrete model for counting events in a fixed number of trials. Additionally, the Poisson process assumes that the events occur randomly and independently, while the binomial distribution requires a fixed probability of success for each trial.

Can the Poisson process be used to model events that occur over a continuous interval of space?

Yes, the Poisson process can be used to model events that occur over a continuous interval of space, as long as the average rate of events remains constant and the events occur randomly and independently.

What are some real-life applications of the Poisson process?

The Poisson process has many real-life applications, such as modeling the number of customers arriving at a store, the number of accidents on a highway, or the number of phone calls received by a call center. It is also commonly used in fields such as finance, biology, and telecommunications.

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