Homework Help is Here – Start Your Trial Now!
Math
Probability
Jointly Gaussian random variables play an important role in probability theory, due partly to the fact that linear combinations of Gaussians are themselves Gaussian. This allows us to answer complex questions by only calculating means and variances. Here, we will explore an application of this phenomenon to the expected value. Let X₁,..., ✗n be independent Gaussian random variables, with expected values E[X¿] = μ and variances Var[X¿] σ² for i = 1,...,n and let 1 n = Y = Σ X; be their average. Intuitively, the random variable Y should get “closer" to μ as n i=1 the number of samples n increases. Below, we will try to make this intuition precise. 1 X1 (a) Define X = Determine its mean vector E[✗] and covariance matrix Σx. Xn (b) Express Y = AX for some matrix A. (Note that row vectors and column vectors are special cases of matrices.) (c) Determine E[Y]. (d) Determine Var[Y]. (e) Calculate the probability that Y is more than 8 > 0 away from its mean, P[|Y − E[Y]| > 6]. Express your answer in terms of the standard normal complementary CDF Q(z) = 1- Þ(z) or the standard normal. (f) Using the fact that Q(3.29) = 2000, calculate how many samples n are needed, as a function of the variance o² to guarantee that P[|Y - E[Y]| > 10] is 1000 or smaller.

Jointly Gaussian random variables play an important role in probability theory, due partly to the fact that linear combinations of Gaussians are themselves Gaussian. This allows us to answer complex questions by only calculating means and variances. Here, we will explore an application of this phenomenon to the expected value. Let X₁,..., ✗n be independent Gaussian random variables, with expected values E[X¿] = μ and variances Var[X¿] σ² for i = 1,...,n and let 1 n = Y = Σ X; be their average. Intuitively, the random variable Y should get “closer" to μ as n i=1 the number of samples n increases. Below, we will try to make this intuition precise. 1 X1 (a) Define X = Determine its mean vector E[✗] and covariance matrix Σx. Xn (b) Express Y = AX for some matrix A. (Note that row vectors and column vectors are special cases of matrices.) (c) Determine E[Y]. (d) Determine Var[Y]. (e) Calculate the probability that Y is more than 8 > 0 away from its mean, P[|Y − E[Y]| > 6]. Express your answer in terms of the standard normal complementary CDF Q(z) = 1- Þ(z) or the standard normal. (f) Using the fact that Q(3.29) = 2000, calculate how many samples n are needed, as a function of the variance o² to guarantee that P[|Y - E[Y]| > 10] is 1000 or smaller.

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
See similar textbooks
icon
Related questions
Question

Only c, d, e, f please. Thanks!

Jointly Gaussian random variables play an important role in probability theory, due partly to the fact that linear combinations of Gaussians are themselves Gaussian. This allows us to answer complex questions by only calculating means and variances. Here, we will explore an application of this phenomenon to the expected value. Let X₁,..., ✗n be independent Gaussian random variables, with expected values E[X¿] = μ and variances Var[X¿] σ² for i = 1,...,n and let 1 n = Y = Σ X; be their average. Intuitively, the random variable Y should get “closer" to μ as n i=1 the number of samples n increases. Below, we will try to make this intuition precise. 1 X1 (a) Define X = Determine its mean vector E[✗] and covariance matrix Σx. Xn (b) Express Y = AX for some matrix A. (Note that row vectors and column vectors are special cases of matrices.) (c) Determine E[Y]. (d) Determine Var[Y]. (e) Calculate the probability that Y is more than 8 > 0 away from its mean, P[|Y − E[Y]| > 6]. Express your answer in terms of the standard normal complementary CDF Q(z) = 1- Þ(z) or the standard normal. (f) Using the fact that Q(3.29) = 2000, calculate how many samples n are needed, as a function of the variance o² to guarantee that P[|Y - E[Y]| > 10] is 1000 or smaller.
Transcribed Image Text:Jointly Gaussian random variables play an important role in probability theory, due partly to the fact that linear combinations of Gaussians are themselves Gaussian. This allows us to answer complex questions by only calculating means and variances. Here, we will explore an application of this phenomenon to the expected value. Let X₁,..., ✗n be independent Gaussian random variables, with expected values E[X¿] = μ and variances Var[X¿] σ² for i = 1,...,n and let 1 n = Y = Σ X; be their average. Intuitively, the random variable Y should get “closer" to μ as n i=1 the number of samples n increases. Below, we will try to make this intuition precise. 1 X1 (a) Define X = Determine its mean vector E[✗] and covariance matrix Σx. Xn (b) Express Y = AX for some matrix A. (Note that row vectors and column vectors are special cases of matrices.) (c) Determine E[Y]. (d) Determine Var[Y]. (e) Calculate the probability that Y is more than 8 > 0 away from its mean, P[|Y − E[Y]| > 6]. Express your answer in terms of the standard normal complementary CDF Q(z) = 1- Þ(z) or the standard normal. (f) Using the fact that Q(3.29) = 2000, calculate how many samples n are needed, as a function of the variance o² to guarantee that P[|Y - E[Y]| > 10] is 1000 or smaller.
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 1 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer