Respuesta :
Answer:
[tex]P(X_1,X_2) = \left \{ {{p^2(1-p)^{k+l}, \quad k,l = 0,1,2,\ldots} \atop {0, \quad \text{otherwise}}} \right.[/tex]
Step-by-step explanation:
Let [tex]X_1[/tex] be a random variable that counts the number of failures preceding the first success and [tex]X_2[/tex] be a random variable that counts the number of failures between the first two successes. The probability of success in a independent trial is [tex]p[/tex] and both of them have Bernoulli distribution.
Assume that the number of unsuccessful trials before the first successful one is [tex]k[/tex] and that [tex]l[/tex] is a number of failures between two successes.
The joint mass function of two discrete random variables [tex]X[/tex] and [tex]Y[/tex] is defined by
[tex]P_{XY}(x,y) = P(X=x,Y=y)[/tex]
In this case, we have two discrete random variables [tex]X_1[/tex] and [tex]X_2[/tex] which are independent, so we have that their joint mass function is of the form
[tex]P_{X_1,X_2}(x,y) = P(X_1=x,X_2=y) = P(X_1=x) \cdot P(X_2=y)[/tex]
The probability that the number of failures before the first successful trial is equal to [tex]k[/tex] is
[tex]P(X_1=k) = \underbrace{(1-p)^k}_{\text{in $k$ trials a failure}} \cdot \underbrace{p}_{\text{after that,a success}}[/tex]
The probability that the number of failures between two successes is equal to [tex]l[/tex] is
[tex]P(X_2 = l) = \underbrace{p}_{\text{a success}} \cdot \underbrace{(1-p)^{j}}_{\text{$j$ failures}} = p(1-p)^j[/tex]
Therefore,
[tex]P(X_1,X_2)= P(X_1=k) \cdot P(X_2=l) \\\phantom{P_{X_1,X_2} \; =} = (1-p)^k \cdot p \cdot p (1-p)^l \\\phantom{P_{X_1,X_2} \; =} = (1-p)^{k+l} \cdot p^2[/tex]
Now, we obtain that that their joint mass function is
[tex]P(X_1,X_2) = \left \{ {{p^2(1-p)^{k+l}, \quad k,l = 0,1,2,\ldots} \atop {0, \quad \text{otherwise}}} \right.[/tex]
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