How Does the Poisson Process Model Customer Arrivals Over Time?

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Discussion Overview

The discussion focuses on the Poisson process as a model for customer arrivals over time, specifically examining the probabilities of different numbers of arrivals in a small interval. Participants explore mathematical expressions related to the Poisson process and seek clarification on certain terms and concepts.

Discussion Character

  • Mathematical reasoning, Technical explanation

Main Points Raised

  • One participant presents the probabilities for one arrival, more than one arrival, and no arrivals in a small interval, referencing the Poisson process with parameter \(s\).
  • Another participant elaborates on the derivation of the probability of one arrival, using the mean arrival rate and discussing the relationship between the probabilities and the truncation error in the series expansion.
  • Questions arise regarding the term \(R(sh)\), with participants seeking clarification on its meaning and significance in the context of the series expansion.
  • A later reply defines \(R(sh)\) as the remainder of the series after truncation, equating it to the sum of the tail of the infinite series.

Areas of Agreement / Disagreement

Participants generally agree on the mathematical framework of the Poisson process and the expressions for arrival probabilities, but there is some uncertainty regarding the interpretation of specific terms like \(R(sh)\).

Contextual Notes

The discussion involves assumptions about the smallness of \(h\) and the behavior of the series expansion, which are not fully resolved. The dependence on the parameter \(s\) and the implications of truncation errors are also noted but not definitively concluded.

Poirot1
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Let customers arrive according to a poisson process with parameter st and let $X_{t}$ denote number of customers in the system by time t. Consider an interval [t,t+h] with h small.

Show that P(1 arrival)= sh + o[h], P(more than one arrival)=o[h] and P(no arrival)=1-sh+o[h].

I know P(1 arrival)=$she^{-sh}$ but how to get further?

Thanks
 
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Poirot said:
Let customers arrive according to a poisson process with parameter st and let $X_{t}$ denote number of customers in the system by time t. Consider an interval [t,t+h] with h small.

Show that P(1 arrival)= sh + o[h], P(more than one arrival)=o[h] and P(no arrival)=1-sh+o[h].

I know P(1 arrival)=$she^{-sh}$ but how to get further?

Thanks

We have a Poisson process with mean arrival rate \(s\), then the number of arrivals in an interval of length \(h\) is \(N\) and \( N \sim P(sh)\).

Then:

\( \displaystyle p(N=1)=sh\; e^{-sh} =sh \left( 1-sh + \frac{s^2h^2}{2}-... \right) = sh(1 + R(sh))=sh + shR(sh)\)

where the \(|R(sh)|\) for small positive \(h\) is bounded by \(sh\) (the truncation error for an alternating series of terms of decreasing absolute value is bounded by the absolute value of the first neglected term), so:

\( \displaystyle |sh \;R(sh)| < s^2h^2 = o ( h)\)

since \( \lim_{h \to 0} (s^2 h^2)/h = 0 \).

Now do the no arrivals case, then \(p(N>1)=1-(p(N=0)+p(N=1))\)

CB
 
Thanks, but can you explain what R(sh) is ?
 
Poirot said:
Thanks, but can you explain what R(sh) is ?

It is the remainder when the series is truncated after the first term, or the sum of the tail of the infinite series (they are the same thing).

CB
 
Ok I can easily do the rest now. Thanks
 

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