Asked by A
Let Θ be an unknown random variable that we wish to estimate. It has a prior distribution with mean 1 and variance 2. Let W be a noise term, another unknown random variable with mean 3 and variance 5. Assume that Θ and W are independent.
We have two different instruments that we can use to measure Θ. The first instrument yields a measurement of the form X1=Θ+W, and the second instrument yields a measurement of the form X2=2Θ+3W. We pick an instrument at random, with each instrument having probability 1/2 of being chosen. Assume that this choice of instrument is independent of everything else. Let X be the measurement that we observe, without knowing which instrument was used.
Give numerical answers for all parts below.
E[X]= ?
E[X2]= ?
The LLMS estimator of Θ given X is of the form aX+b. Give the numerical values of a and b.
a= ?
b= ?
We have two different instruments that we can use to measure Θ. The first instrument yields a measurement of the form X1=Θ+W, and the second instrument yields a measurement of the form X2=2Θ+3W. We pick an instrument at random, with each instrument having probability 1/2 of being chosen. Assume that this choice of instrument is independent of everything else. Let X be the measurement that we observe, without knowing which instrument was used.
Give numerical answers for all parts below.
E[X]= ?
E[X2]= ?
The LLMS estimator of Θ given X is of the form aX+b. Give the numerical values of a and b.
a= ?
b= ?
Answers
Answered by
Anonymus
E[X]=7.5
Answered by
Anonymous
E[X^2] = 97
Answered by
qwerty
a=?
b=?
b=?
Answered by
anonymous
a and b ?
Answered by
Mary
a and b ?????
Answered by
anonymous
How did you get 97?
Answered by
Anonym
If anybody knows how to get the E[X^2] can you enlighten us please? I am just really thrown off by the fact that it has 2 ways of measuring X.
Answered by
Anonym
E[X]=7.5
E[X^2]=98.5
a=0.071
b=0.467
E[X^2]=98.5
a=0.071
b=0.467
Answered by
Anon
re: E[X^2]:
Remember that Var[X] = E[X^2]-(E[X])^2 and also that (in short-hand) Var[X] = E[Var] + Var[E]. Use this last identity to get Var[X]; we already have E[X] so we can easily get E[X^2].
Remember that Var[X] = E[X^2]-(E[X])^2 and also that (in short-hand) Var[X] = E[Var] + Var[E]. Use this last identity to get Var[X]; we already have E[X] so we can easily get E[X^2].
Answered by
Anonymous
correct answer for E[X^2] is 98.5
(NOT 97)
(NOT 97)
Answered by
Sam
b=0.471
Answered by
cle
Can someone explain me how to find E[X]? thanks in advance
Answered by
Anonymous
you can find E[X] using the law of total expectation
There are no AI answers yet. The ability to request AI answers is coming soon!
Submit Your Answer
We prioritize human answers over AI answers.
If you are human, and you can answer this question, please submit your answer.