 Quote by chiro
One brief example that I can give is customer type.
Customers that go to a particular store may be frequent customers or they may be customers that visit at particular times of the day depending on what keeps them occupied.
For example 9-5 workers may get time to shop after work and not during the day where-as retired people, students, stay at home people, etc may go during the day.
What is sold will also affect the clients and arrivals.
The season will also affect things (Valentines Day with chocolates and Roses).
Also you must consider what is sold: something like a petrol or gas station will have very different properties and arrival times to even that of a super-market.
These are a few examples highlighting how things can cause dependencies between observations and other factors.
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Those are all examples of why the rate parameter may vary with time, but not of why one arrival may alter the probability (given the time of day) of another. Instead, consider that some people shop in pairs or even larger groups. Cluster arrivals would be distinctly non-Poisson.