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Exercise
May 07'23
Answer
Solution: E
First, find the conditional probability function of [math]N_2[/math] given [math]N = n_1: p_{2|1}(n_2 | n_1) = \frac{p(n_1,n_2)}{p_1(n_1)}[/math] where [math]p_1(n_1) [/math] is the marginal probability function of [math]N_1[/math]. To find the latter, sum the joint probability function over all possible values of [math]N_2[/math] obtaining
[[math]]
p_1(n_1) = \sum_{n_2 =1}^{\infty} p(n_1,n_2) = \frac{3}{4} \left (\frac{1}{4} \right)^{n_1-1} \sum_{n_2=1}^{\infty} (1-e^{-n_1})^{n_2-1} = \frac{3}{4}\left ( \frac{1}{4}\right )^{n_1-1},
[[/math]]
which is the probability function of a geometric random variable with parameter [math]p=e^{-n_1} [/math]. The mean of this distribution is [math] 1/p = 1/e^{-n_1} = e^{n_1}[/math] and becomes [math]e^2[/math] when [math]n_1 = 2 [/math].