A problem related to Poisson process

In summary, the problem being discussed is about finding the distribution in which at least one customer arrives at time t in a queueing system with n customers arriving according to Poisson processes with rate Lamda. The solution involves conditioning on the number of customers that have arrived and using the law of total probability. It is also mentioned that the processes are mutually independent and homogeneous Poisson processes with rate Lamda. The correct solution involves calculating the probability of no customers arriving by time t and using the exponential distribution with parameter N*lambda.
  • #1
quacam09
16
0
Hi all, I have a probability problem. Can you help me? Thank you!

Here is the problem:

Consider the queueing system, there are n customers 1, 2, ...N.
Customer 1 arrives in accordance with a Poisson process with rate Lamda, customer 2 arrives in accordance with a Poisson process with rate Lamda,..., customer N arrives in accordance with a Poisson process with rate Lamda .

What is the distribution in which at least one customer arrive at time t?
 
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  • #2
You can condition on the number of customers that have arrived and the exponential holding times of the Poisson process, then use the law of total probability.
 
  • #3
Focus said:
You can condition on the number of customers that have arrived and the exponential holding times of the Poisson process, then use the law of total probability.

Thank you for your response! Can you explain your idea in detail?

Is the following solution correct?

Consider the queueing system, there are n customers 1, 2, ...N.
Customer 1 arrives in accordance with a Poisson process with rate Lamda, customer 2 arrives in accordance with a Poisson process with rate Lamda,..., customer N arrives in accordance with a Poisson process with rate Lamda .

N processes are mutually independent and homogeneous Poisson processes with rate Lamda

=> at least one of custumers arrive the system, which occurs at the rate Lamda*N
 
  • #4
quacam09 said:
Thank you for your response! Can you explain your idea in detail?

Is the following solution correct?

Consider the queueing system, there are n customers 1, 2, ...N.
Customer 1 arrives in accordance with a Poisson process with rate Lamda, customer 2 arrives in accordance with a Poisson process with rate Lamda,..., customer N arrives in accordance with a Poisson process with rate Lamda .

N processes are mutually independent and homogeneous Poisson processes with rate Lamda

=> at least one of custumers arrive the system, which occurs at the rate Lamda*N

Yes but that is given that there is no one in the queue. Given that k people are in the queue, the arrival rate is (N-k)*lambda (k not greater than N). Now you have to use [itex] \mathbb{P}(A)=\sum_{k \in \mathbb{N}}\mathbb{P}(A|B=k)\mathbb{P}(B=k)[/itex].

Sorry do you actually mean the probability of at least one customer arrive by time t? At time t, you can't have two customers arriving.
 
  • #5
Focus said:
Yes but that is given that there is no one in the queue. Given that k people are in the queue, the arrival rate is (N-k)*lambda (k not greater than N). Now you have to use [itex] \mathbb{P}(A)=\sum_{k \in \mathbb{N}}\mathbb{P}(A|B=k)\mathbb{P}(B=k)[/itex].

Sorry do you actually mean the probability of at least one customer arrive by time t? At time t, you can't have two customers arriving.

Sorry, "At least one customer" mean we can have one, two, ...or N customer arrive by time t. N processes are mutually independent and homogeneous Poisson processes with rate Lamda. So at time t, we can have two customers arriving.
 
  • #6
Ok well then work out the probability of no customers arriving by time t. If N people are arriving with independent Poisson, then you have N*lambda Poisson, so the probability of no people arriving by time t is exp distributed with parameter N*lambda. This is due to the holding times of Poisson processes (that is the times between jumps) are exponential.

Sorry if my posts aren't making sense, I have been somewhat tired recently.
 
  • #7
Focus said:
Ok well then work out the probability of no customers arriving by time t. If N people are arriving with independent Poisson, then you have N*lambda Poisson, so the probability of no people arriving by time t is exp distributed with parameter N*lambda. This is due to the holding times of Poisson processes (that is the times between jumps) are exponential.

Sorry if my posts aren't making sense, I have been somewhat tired recently.

OK. Thank you for your help!
 

1. What is a Poisson process?

A Poisson process is a mathematical model used to describe the occurrence of events over time. It is a stochastic process, meaning that the timing of events is random, and the number of events that occur in a given time interval follows a Poisson distribution.

2. What are some real-life examples of a Poisson process?

Some real-life examples of a Poisson process include the number of customers arriving at a store, the number of phone calls received by a call center, and the number of accidents on a highway in a given time period.

3. How is a Poisson process different from a normal distribution?

A Poisson process is different from a normal distribution in that it models the number of events occurring in a given time interval, whereas a normal distribution models continuous data. Additionally, a Poisson process assumes that events occur independently of each other, while a normal distribution assumes that events are dependent on each other.

4. Can a Poisson process be used for non-time based events?

Yes, a Poisson process can be used to model non-time based events as long as the events occur independently of each other and follow a Poisson distribution. For example, it can be used to model the number of defects in a product produced by a factory.

5. How can a Poisson process be applied in research or data analysis?

A Poisson process can be applied in research or data analysis to model the occurrence of events and make predictions about future events. It can also be used to test hypotheses about the rate of events occurring, such as whether a certain intervention has an effect on the rate of customer arrivals or the number of accidents on a particular highway.

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