How Does the Poisson Process Model Customer Arrivals Over Time?

In summary: 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).
  • #1
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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  • #2
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
 
  • #3
Thanks, but can you explain what R(sh) is ?
 
  • #4
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
 
  • #5
Ok I can easily do the rest now. Thanks
 

Related to How Does the Poisson Process Model Customer Arrivals Over Time?

What is a Poisson process derivation?

A Poisson process derivation is a mathematical method used to describe the behavior of a random process that occurs at a constant rate and independently of any previous occurrences. It is often used in fields such as statistics, physics, and finance to model events that happen randomly over a period of time.

What are the assumptions made in a Poisson process derivation?

The assumptions made in a Poisson process derivation include a constant rate of occurrence, independence of events, and a fixed time period for observation. Additionally, the events must be mutually exclusive, meaning that they cannot occur simultaneously.

How is a Poisson process derivation calculated?

A Poisson process derivation is typically calculated using the Poisson distribution formula, which takes into account the rate of occurrence and the number of events observed within a fixed time period. Alternatively, it can also be calculated using differential equations or generating functions.

What is the difference between a homogeneous and non-homogeneous Poisson process derivation?

A homogeneous Poisson process derivation assumes that the rate of occurrence is constant over time, while a non-homogeneous process allows for the rate to vary. This means that a non-homogeneous process may have a time-dependent rate or may have different rates in different regions or sub-intervals.

What are some real-world applications of Poisson process derivation?

Poisson process derivation has various applications in fields such as queuing theory, inventory management, and telecommunications. It is also used to model phenomena such as radioactive decay, insect populations, and customer arrivals in a service system.

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