Undergrad Why do we require conditions for the Poisson Distribution?

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The Poisson Distribution requires three key conditions for valid application: a constant average count rate over time, independence of counts, and the probability of two or more counts occurring in a given interval being zero. The first two conditions stem from the fundamental definition of the distribution, ensuring that the data reflects a true Poisson process. The third condition, while less clear, relates to the discrete nature of the distribution, indicating that multiple events in a short interval should not occur. If these conditions are not met, the data cannot be accurately modeled using the Poisson Distribution. Understanding these requirements is crucial for proper statistical analysis.
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Three conditions must be met in order for the Poisson Distribution to be used:

1) The average count rate is constant over time
2) The counts occurring are independent
3) The probability of 2 or more counts occurring in the interval $n$ is zero

Simply, why must these conditions be met for valid use of the Poisson Distribution?
 
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The first two are based on the definition. I am not sure what the third condition is, but being independent the counts may be arbitrarily close..
 
3) seems to be something related to some discrete approximation to the Poisson distribution, not pertaing to the proper distribution per se.

The other two are simply properties of the distribution. If they don't hold, the data aren't Poisson distributed in the first place.
 
Time reversal invariant Hamiltonians must satisfy ##[H,\Theta]=0## where ##\Theta## is time reversal operator. However, in some texts (for example see Many-body Quantum Theory in Condensed Matter Physics an introduction, HENRIK BRUUS and KARSTEN FLENSBERG, Corrected version: 14 January 2016, section 7.1.4) the time reversal invariant condition is introduced as ##H=H^*##. How these two conditions are identical?

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