Conditional Expectation Value of Poisson Arrival in Fixed T

In summary: As you say, the probability of m is given by the Poisson formula, Pm=##\frac{(\lambda T)^m}{m!}e^{-\lambda T}##. The summation is therefore##\frac{1}{\Sigma _{m≥n}P_{T,m}}\Sigma _{m≥n}E[n,m,T]P_{T,m}##,which is equivalent to##\Sigma _{m≥n}m\frac{P_{T,m}}{\Sigma _{m≥n}P_{T,m}}##.The bottom line is that I think your solution for the second question (and similarly for the third) is
  • #1
Mehmood_Yasir
68
2
Assume a Poisson process with rate ##\lambda##.

Let ##T_{1}##,##T_{2}##,##T_{3}##,... be the time until the ##1^{st}, 2^{nd}, 3^{rd}##,...(so on) arrivals following exponential distribution. If I consider the fixed time interval ##[0-T]##, what is the expectation value of the arrival time ##1^{st}, 2^{nd}, 3^{rd}... ## i.e.,
1. ##E[T_{1}|T_{1}\le T]## ?
2. ##E[T_{2}|T_{1}<T_{2}\le T]## ?
3. ##E[T_{3}|T_{2}<T_{3}\le T]## ?
My approach! For Poisson process with rate ##\lambda##, each time interval corresponds to a random variable ##X_i## with an exponeitial distribution.Therefore,
\begin{align*}
&T_1=X_1 \\&
T_2=X_1+X_2\\&T_3=X_1+X_2+X3\\&...
\end{align*}
##T_i## has Gamma distribuiotn ##\Gamma(i,\lambda)##. If ##T## is a deterministic value not a random variable. Then, ##E[T_{1}|T_{1}\le T]##
\begin{align*}
E[T_{1}|T_{1}\le T]&=\int_{0}^\infty t_1f_{T_1|T_1 \le T}(t_1)dt_1\\&=\frac{1}{1-e^{-\lambda T}}\int_{0}^T t_1 \lambda e^{-\lambda t_1}dt_1\\&=\frac{-1}{1-e^{-\lambda T}}\int_0^T t_1de^{-\lambda t_1}\\&\text{(Final solution is )}\\&=\frac{-(Te^{-\lambda T}+\frac{1}{\lambda}e^{-\lambda T}-\frac{1}{\lambda})}{1-e^{-\lambda T}}
\end{align*}

What about ##E[T_{2}|T_{1}<T_{2}\le T]## and ##E[T_{3}|T_{2}<T_{3}\le T]##.
Can someone please guide me? I thank in advance.
 
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  • #2
I think this is valid:
Suppose there are exactly m events in [0,T]. The probability of that is PT,m=##\frac{(\lambda T)^m}{m!}e^{-\lambda T}##.
For n≤m, the expected time to the nth of them is ##E[n,m,T]=\frac{nT}{m+1}##. The expected time over all values of m is then ##\frac{\Sigma _{m≥n}E[n,m,T]P_{T,m}}{\Sigma _{m≥n} P_{T,m}}##.

As a check, for large n that approximates ##\frac{nT}{n+1}##, as it should.
 
  • #3
@haruspex many thanks for your answer. I tried to understand it but could not get it completely. Actually we don't know the no. of events in time ##[0-T]##.
Let me put this way. E.g., assume an arrival appear at ##t=0## and starts a clock for a duration of Time ##T##. Now the rest of the arrivals follow poisson process with rate ##\lambda##. When clock time which is ##T## expires, we have to calculate now the expected arrival time of the first, second and third,...so on.
I did for the first arrival which is in fact conditional expectation value of the arrival time i.e., ##E[T_{1}|T_{1}<=T]##.

Since the expected value of the arrival time of the second arrival will be greater than the arrival time of the first arrival and will be less than ##T##. Is this approach CORRECT analogous to my first proposal for determining value of the ##E[T_{2}|T_{1}<T_{2}<=T]## (see the attached image)?
image.png
 

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  • #4
Mehmood_Yasir said:
Actually we don't know the no. of events in time [0−T]
I understand that. If you look at my final expression you will see it sums over all m (total number of events in T) ≥ n.
 
Last edited:

1. What is the formula for calculating the conditional expectation value of Poisson arrival in fixed T?

The formula is E[X|X≤T] = ∑k=0⌊T⌋ kP(X=k)/P(X≤T), where X is the random variable representing the number of arrivals in a given time period and T is the fixed time period.

2. How is the conditional expectation value of Poisson arrival related to the overall mean arrival rate?

The conditional expectation value of Poisson arrival is equal to the overall mean arrival rate multiplied by the fixed time period, T. In other words, it represents the expected number of arrivals within the fixed time period, given that at least one arrival has occurred.

3. Can the conditional expectation value of Poisson arrival be negative?

No, the conditional expectation value of Poisson arrival cannot be negative. It represents a count of events, and a negative count is not meaningful.

4. What factors can influence the value of the conditional expectation value of Poisson arrival?

The value of the conditional expectation value of Poisson arrival can be influenced by the mean arrival rate, the fixed time period, and the probability distribution of the number of arrivals.

5. How can the conditional expectation value of Poisson arrival be used in practical applications?

The conditional expectation value of Poisson arrival can be used in various applications, such as predicting the number of customer arrivals in a given time period to optimize staffing levels, estimating the number of defects in a manufacturing process, or forecasting the number of accidents in a specific time period for insurance purposes.

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