Consider two components and three types of shocks. A type I shock causes component I to fail, a type 2 shock causes component 2 to fail, and a type 3 shock causes both components 1 and 2 to fail. The times until shocks 1, 2, and 3 occur are independent exponential random variables with respective rates λ 1 , λ 2 and λ 3 . Let X i denote the time at which component i fails, i = 1 , 2 . The random variables X 1 , X 2 are said to have a joint bivariate exponential distribution. Find P { X 1 > s , X 2 > t } .
Consider two components and three types of shocks. A type I shock causes component I to fail, a type 2 shock causes component 2 to fail, and a type 3 shock causes both components 1 and 2 to fail. The times until shocks 1, 2, and 3 occur are independent exponential random variables with respective rates λ 1 , λ 2 and λ 3 . Let X i denote the time at which component i fails, i = 1 , 2 . The random variables X 1 , X 2 are said to have a joint bivariate exponential distribution. Find P { X 1 > s , X 2 > t } .
Solution Summary: The author explains the joint probability distribution of X 1, x 2 and the time at which component i fails.
Consider two components and three types of shocks. A type I shock causes component I to fail, a type 2 shock causes component 2 to fail, and a type 3 shock causes both components 1 and 2 to fail. The times until shocks 1, 2, and 3 occur are independent exponential random variables with respective rates
λ
1
,
λ
2
and
λ
3
. Let
X
i
denote the time at which component i fails,
i
=
1
,
2
. The random variables
X
1
,
X
2
are said to have a joint bivariate exponential distribution. Find
P
{
X
1
>
s
,
X
2
>
t
}
.
If X and Y are independent random variables with E(X) = 10, Var(X) = 4,
E(Y) = 12 and VAR(Y) = 3, If W =-2X +Y Find E(W) and STD (W)
(a)
(b)
If X and Y are not independent, with E(X) =10, VAR(X) = 5, E(I) =18,
VAR(Y) = 3 and CovX,Y) = -2. If W = X – 2Y Find E(W) and STD(W)
Let X and Y be two random variables. Suppose we know E(X) = 7 and E(Y)=3.Let Z = X − Y be a random variable. What is E(Z)? Justify your answer.
Derived RVs. Suppose you are running a simulation on a large data set. Assuming that the
task is parallelizable, you can split it into two component tasks and assign them to two worker
nodes and run in parallel. The time for completion at each worker node can be modelled
as random variables X and Y respectively where X and Y are two independent exponential
random variables with parameters A1 and X2 respectively. Let the random variable Z be defined
as the time for completion of the task. Find the CDF and PDF of Z.
Note : We can declare the task as complete only after the computation at both the worker
nodes is complete.
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