1. Consider discrete random variables X and Y [BLAK 1979] with the joint pmf as shown below: Y X -1 0 1 -2 -1 1 2 16 16 Are X and Y independent? Are they uncorrelated? -19 11011816 -12-19-12-19 --10-10-19 16 16 16
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- 2A QR code photographed in poor lighting, so that it can be difficult to distinguish black and white pixels. The gray color (X) in each pixel is therefore coded on a scale from 0 (white) to 100 (black). The true pixel value (without shadow) the code is Y = 0 for white, and Y = 1 for black. We treat X and Y as random variables. For the highlighted pixel in the figure is the gray color X = 20 and the true pixel value is white, i.e. Y = 0. We assume that QR codes are designed so that, on average, there are as many white as black pixels, which means that pY (0) = pY (1) = 1/2. In this situation, X is continuously distributed (0 ≤ X ≤ 100) and Y is discretely distributed, but we can still think about the simultaneous distribution of X and Y. We start by defining the conditional density of X, given the value of Y : fX|Y(x|0) = "Pixel is really white" fX|Y(x|1) =" Pixel is really balck " Use Bayes formula as given in the picture and find the probability for x = 20 like in the picture.show that if X11, X12,..., X1, X21, X22,..., X2n₂ are independent random variables, with the first n constitut- ing a random sample from an infinite population with the mean μ₁ and the variance of and the other n2 constitut- ing a random sample from an infinite population with the mean μ2 and the variance o2, then 1 M2 (a) E(X₁-X₂) = μ₁ −μ2; 07 0 22 (b) var(X₁-X₂) = + n₁ n₂
- 1. The random variables §, §1, §2,... are independent and identically distributed with 1/4 and P(§ = j) = c/j for j = 1,2,3. Let X₁ = 0 and Xn = max(§₁, ..., Èn) for n = 1, 2,.... distribution P(§ = 0) = (a) What value must c take? (b) Explain why {Xn, n = 0, 1, 2,...} is a Markov chain. (c) Write down the transition matrix. (d) Draw the transition diagram and classify the states (aperiodic, transient, re- current, eorgodic, etc). (e) Calculate P(Xn = 0). (f) Calculate P(X₁ = 3, X₂ = 1|X₁ = 3).If a variable can take certain integer values between two given points, then it is called O a. Continuous random variable O b. Probabilistic random variable c. O c. Deterministic random variable O d. Uncertain random variable O e. Discrete random variable8) Consider the following Random Variable X X = k Pr(X=k) 3 .2 7 .1 1 .4 .2 .1 Find the following: a) Pr(X > 4) b) E(X) c) V(X)
- 2. Some properties of Expected value and variance of a random variable. a) Assume that X is an arbitrary discrete random variable, and a and b are constant. Using the definitic Show: and E(aX + b) = a · E(X) + b V(aX + b) = a² · V(X ) Stat 3128 Ali Mahzarnia P STAT 3128 Ali Mahzarnia b) Justify the computational formula of Variance of a random variable which is to justify : V(X) = E[(X – µ°] = Ex – µ)P • ptx) = | 2: Here needs justification By Cauchy Schwarz inequality it can be shown that the right hand side is always positive. Analogs expression in Mechanic : Parallel axis theorem Iem = I– md² Moment of Moment of inetria about Inertia of an an axis shifted object about the center of a mass by d from center of mass (a parallel shift) Icm is dispersion around the mean and is like second central moment (variance) I is like second moment if d is mean m is like sum total all the weight of each of the x which all add up to 1 d squared is like squared of mean since we,Suppose T and Z are random variables. a. If P(T> 2.98)= 0.03 and P(T0.53) = P(Z0.53). a. P(-2.98STS2.98) =| Enter your answer in the answer box and then click Check Answer. 1 part remaining Clear All MacBook Air F3 000 F4 F72. Let x be a binomial random variable with n=20 and p=.3. Which of the following gives p(x = 13)? • (a) Use binomial table for n=20, p=0.3, and k = 13 • (b) Use binomial table for n=20, p=0.3, and k = 12 • (c) Subtract (b) from (a), i.e. p(x= 13) = p(x ≤ 13) – p(x ≤ 12) PLEASE SHOW ME HOW TO DO IT SO I CAN LEARN IT?
- Suppose X is a discrete random variable. Let the pmf of X be equal to 5 - x f(x) = x = 1,2,3,4 10 Find the cdf of X, that is F(X).1 0:0E HW3.pptx > Q1:Show that: If X1, X2, ,X, are independent random variables and X = X1 + X2 + + X, , then Q2: What is the expectation and the veriance of RV X, where X represents the out come throwing a die? 03: Find the expectation and the variance of X, where X is binomial random variable X - Binomial(n, p), Var(X)? 1. Chapnera Q4: Let X be a discrete random variable with range Rx = {1, 2, 3, ...}. Suppose the PMF of X is given by Px (k) = 1 for k = 1, 2, 3, ... a) Find and plot the CDF of X, Fx(x). b) Find P(1Example 2.14. Show the CDF of the random variable X with the following pdf: Sx(2) = lwa(x) Example 2.15. Let X be a random variable with the following pdf: 1. Find its CDF. 2. Evaluate the following probabilities using its CDF and/or pdf. a) P(} 1) c) P(X > }|X < 1)SEE MORE QUESTIONS