Finite Mathematik Beispiele

Eine diskrete Zufallsvariable ${\displaystyle x}$ nimmt eine Menge separater Werte (wie ${\displaystyle 0}$, ${\displaystyle 1}$, ${\displaystyle 2}$...) an. Ihre Wahrscheinlichkeitsverteilung weist jedem möglichen Wert ${\displaystyle x}$ eine Wahrscheinlichkeit ${\displaystyle P\left(x\right)}$ zu. Für jedes ${\displaystyle x}$ nimmt die Wahrscheinlichkeit ${\displaystyle P\left(x\right)}$ einen Wert im abgeschlossenen Intervall mit den Grenzen ${\displaystyle 0}$ und ${\displaystyle 1}$ an und die Summe der Wahrscheinlichkeiten für alle möglichen ${\displaystyle x}$ ist gleich ${\displaystyle 1}$.

${\displaystyle -13}$ is not greater than or equal to ${\displaystyle 0}$, which doesn't meet the first property of the probability distribution.

${\displaystyle -31}$ is not greater than or equal to ${\displaystyle 0}$, which doesn't meet the first property of the probability distribution.

${\displaystyle -59}$ is not greater than or equal to ${\displaystyle 0}$, which doesn't meet the first property of the probability distribution.

${\displaystyle -97}$ is not greater than or equal to ${\displaystyle 0}$, which doesn't meet the first property of the probability distribution.

${\displaystyle -146}$ is not greater than or equal to ${\displaystyle 0}$, which doesn't meet the first property of the probability distribution.

Die Wahrscheinlichkeit ${\displaystyle P\left(x\right)}$ fällt für alle ${\displaystyle x}$-Werte nicht in das geschlossene Intervall von ${\displaystyle 0}$ bis ${\displaystyle 1}$, was der ersten Bedingung der Wahrscheinlichkeitsverteilung widerspricht.