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Die Zufallsvariablen X und U sind diskret. U ist auf {1,2} gleichverteilt, bedeutet f_U(u) = 0,5
Bedingt auf U = 1 und U = 2, sei X Poisson-verteilt mit Parametern lambda_1 > 0 bzw. lambda_2 > 0, d.h.
f_X|U(x|1) =
, x ist natürliche Zahl inkl. 0
f_X|U(x|2) =
, x ist auch hier eine natürliche Zahl inkl. 0, also kann die Werte annehmen...
Aufgabe: Wie lautet die marginale Wahrscheinlichkeitsfunktion von X?
Mein Ansatz war soweit:
f_X,U(x,u) = f_U,X(u,x)
Ich habe f_U(u) = 0,5 gegeben, es ist f_X(x) gesucht.
Um f_X(x) zu berechnen, habe ich die Gleichung umgestellt zu: f_X(x) = f_U,X(u,x) / f_U|X(u|x).
Da ich aber f_U,X(u,x) nicht gegeben habe, habe ich es berechnet:
Dadurch habe ich f_X|U(x|1) * 0,5 + f_X|U(x|2) * 0,5 gerechnet:
f_X,U(x,u) = f_U,X(u,x) 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)
ist dann das Ergebnis für f_X,U(x,u). Danach ist mir aufgefallen, dass ich durch f_U|X(u|x) teilen muss, ich aber nur f_U|X(x|u) habe, wodurch ich dann versucht habe mit dem Satz von Bayes f_U|X(x|u) zu berechnen, was aber nicht geklappt hat. Wie kann ich f_U|X(u|x) berechnen, um dann f_X(x) zu berechnen?
Bedingt auf U = 1 und U = 2, sei X Poisson-verteilt mit Parametern lambda_1 > 0 bzw. lambda_2 > 0, d.h.
f_X|U(x|1) =
f_X|U(x|2) =
Aufgabe: Wie lautet die marginale Wahrscheinlichkeitsfunktion von X?
Mein Ansatz war soweit:
f_X,U(x,u) = f_U,X(u,x)
Ich habe f_U(u) = 0,5 gegeben, es ist f_X(x) gesucht.
Um f_X(x) zu berechnen, habe ich die Gleichung umgestellt zu: f_X(x) = f_U,X(u,x) / f_U|X(u|x).
Da ich aber f_U,X(u,x) nicht gegeben habe, habe ich es berechnet:
Dadurch habe ich f_X|U(x|1) * 0,5 + f_X|U(x|2) * 0,5 gerechnet:
f_X,U(x,u) = f_U,X(u,x) =
ist dann das Ergebnis für f_X,U(x,u). Danach ist mir aufgefallen, dass ich durch f_U|X(u|x) teilen muss, ich aber nur f_U|X(x|u) habe, wodurch ich dann versucht habe mit dem Satz von Bayes f_U|X(x|u) zu berechnen, was aber nicht geklappt hat. Wie kann ich f_U|X(u|x) berechnen, um dann f_X(x) zu berechnen?
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