Exercise 10.4. Let X be a Poisson random variable with parameter λ. That is, P(X = k) e-λλk/kl, k...
Let X be a Poisson random variable with parameter λ = 6, and let Y = min(X, 12). (a) What is the p.m.f. of X? (b) What is the mean of X? (c) What is the variance of X? (d) What is the p.m.f. of Y? (e) Compute E[Y ].
5. Let X be a Poisson random variable with parameter λ = 6, and let Y = min(X, 12). (a) What is the p.m.f. of X? (b) What is the mean of X? (c) What is the variance of X? (d) What is the p.m.f. of Y? (e) Compute EY
5. Let X be a Poisson random variable with parameter λ 6, and let Y-min(X,12 (a) What is the p.m.f. of X? (b) What is the mean of X? (c) What is the variance of X? (d) What is the p.m.f. of Y? (e) Compute E[Y].
9. Let X be a Poisson random variable with parameter k = 3. (a) P[X 25] (b) Find P[5 S X <10) (c) Find the variance ? 10. Use the related Table to find the following: (here Z represents the standard normal variable) (a) P[Z > 2.57] (b) The point z such that PL-2 SZ sz]=0.8
Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability mass function of X Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability mass function of X is r E 0,1,2,...) This distribution is often used to model the number of events which will occur in a given time span, given that λ such events occur on average a) Prove by direct computation that the mean of...
Exercise 2.23 If X is a discrete random variable having the Poisson distribution with parameter that the probability that X is even is e cosh A. Exercise 2.24 If X is a discrete random variable having the geometric distribution with parameter p. show that the probability that X is greater than k is (1 -p)k à, show
3, Let X be a Poisson random variable with parameter λ. Calculate the conditional expectation of X given that X is odd.
Compute the expected value of the Poisson distribution with parameter λ X ∼ Poisson(λ). Show E[X(X − 1)(X − 2)· · ·(X − k)] = λ ^(k+1) Use this result, and that in question above, to calculate the variance of X
Let M be a Poisson (λ) random variable having M equal m. If we flip a p-biased coin m times and let X be the number of heads, show that X is a Poisson (pλ) random variable. Use the identity for k= 0 to infinity Σy^k/k! =e^y
(a) Let YA ~ P(λ) denote a Poisson RV with parameter λ. For a non-random function b(A) > 0, consider the the RVs Xx:-b(A)(YA-A), λ > 0. Use the method of ChFs to find a function b(A) such that XA 1 X as λ 00, where X is a non-degenerate RV. You are expected to establish the fact of convergence and specify the distribution of X ,IE [0,oo)? Explain. (b) Does the distribution of y, converge as ג Hint: (a)...