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Problem 2. This problem investigates the similarity between the geometric and ex- ponential random variables observed last week. Let Y be a geometric random variable with parameter p, so that Y represents the trial number of the first success in a sequence of independent Bernoulli trials. Suppose the trials occur at times , 2, . . . , and that δ and p are both very small. Let λ /δ. At time t, about t/6 trials have taken place (a) Compute P(Y > m), which represents the probability that no success has been observed by time t to e-M as p, δ 0 with λ p/S fixed. variable with parameter λ. (b) Show that the probability that no success has been observed by time t converges (c) Conclude that the time of the first success is approximately an exponential random

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Answer #1

(a)

At time t = mdelta, total number of trials is t/delta = mdelta / delta = m

By CDF of geometric distribution, probability of no success till m trials is,

P(Y > m ) = (1 - p)^m

(b)

Let T be the time of first success. Then the probability that no success has been observed by time t is,

P(T > t) = P(Y > m)

As, delta ightarrow 0 and p ightarrow 0

P(T > t) = P(Y > m) = lim_{delta ightarrow 0 ~p ightarrow 0} (1 - p)^m = lim_{delta ightarrow 0 } (1 - delta lambda)^m {lambda = p /delta}

= lim_{delta ightarrow 0 } (1 - delta lambda)^{t/delta} {t = mdelta}

= lim_{delta ightarrow 0 } (1 + (-delta lambda))^{(-tlambda)/((-delta lambda))}

=e^{-t lambda} {  lim_{x ightarrow 0} (1 + x)^{k/x} = e^k }

(c)

From part (b),

P(T > t)=e^{-t lambda}

Rightarrow P(T le t)=1 - e^{-t lambda}

Rightarrow F(t)=1 - e^{-t lambda} where F(t) is the CDF of T.

PDF of t is,

f(t)= F'(t)=lambda e^{-t lambda}

which is the PDF of exponential distribution with the parameter lambda.

Thus, the time of first success T is approximately an exponential distribution variable with parameter lambda​​​​​​​.

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