Poisson Process Conditonal Probabilities

In summary, the conversation is about finding the expected value of a Poisson process with a given rate and time, given that at least one event has occurred. The best way to approach this is to find the conditional distribution first. The formula for the conditional probability is P(Xt=z | Xt≥1) = (e-λt * (λt)z/z!) / (1 - e-λt) for z=1, 2..., which can also be obtained by subtracting the probability of no events from 1. To get the expected value under the condition, it needs to be divided by (1 - e-λt).
  • #1
andrew21nz
2
0
Hey

I'm really struggling with this:

What is the expected value of a poisson process (rate λ, time t) given that at least one even has occured? I was told the best way was to find the conditional distribution first.

So this is: P(Xt=z | Xt≥1)

= P(Xt=z, Xt≥1) / (PXt≥1)

= P(Xt=z) / (PXt≥1) for z=1, 2...

= (e-λt * (λt)z/z!)
-----------------
... 1 - e-λt

That's what I keep coming back to. I think it should come out as something poisson? That way the expected value is just the parameter. Clearly I'm doing something wrong and it's really frustrating me

Sorry if it's messy, I couldn't get the Latex thing working

Thanks in advance for your help :)
 
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  • #2
The conditional probability of getting at least one is correct. The easiest way to do it is subtract the probability that none occurred from 1, which gives the same result you got. To complete the problem you need to get expected value under the condition. This will be the unconditional expected value divided by (1 - e-λt).
 
  • #3
Oh true, because the expected value is an integral depending on z so the denominator just comes out the front.

Thank you for your help
 

Related to Poisson Process Conditonal Probabilities

1. What is a Poisson process?

A Poisson process is a mathematical model used to describe the random occurrence of events over time. It is a continuous-time stochastic process where events occur independently and at a constant rate. It is often used in areas such as queuing theory, telecommunications, and reliability analysis.

2. How are conditional probabilities used in a Poisson process?

Conditional probabilities are used in a Poisson process to calculate the probability of a certain number of events occurring within a specific time interval, given that a certain number of events have already occurred in a different time interval. This can be useful in predicting the likelihood of future events based on past observations.

3. What is the formula for calculating conditional probabilities in a Poisson process?

The formula for calculating conditional probabilities in a Poisson process is P(X = k | Y = n) = (λ^k * e^-λ) / (k! * (1 - e^-λ)^n), where λ is the rate parameter and k and n are the number of events occurring in different time intervals.

4. Can a Poisson process have a non-integer rate parameter?

Yes, a Poisson process can have a non-integer rate parameter. In fact, the rate parameter can take on any positive real value. This allows for a more flexible and accurate model in situations where events do not occur at a constant rate.

5. How is the rate parameter related to the mean and variance in a Poisson process?

The rate parameter (λ) in a Poisson process is directly proportional to both the mean and variance of the process. This means that as the rate parameter increases, so does the mean and variance. The mean and variance of a Poisson process are both equal to λ.

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