1.6 Let Y, and Y2 have the joint probability density function given by<(1–0 < y1 < y2 S10,elsewhere1.6.1 What value of k makes f(y1,Y2) a joint probability density function. I1.6.2 Determine the distribution of f2(Y2). /1

Introduction: Joint Probability density function: The joint Probability density function is the…

Answered: 1.6 Let Y, and Y2 have the joint…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Let f, g be probability densities such supp(f) c supp(g). show yes i.i.d. X1,..., X, L(g) then f(X;) Wi g(X;) is hopeful 1. Argue why the weights need to be re-normalized even when the normalization constants of the densities f and g are known.
please solve c part
6.74 Let Y₁, ₂, ..., Y, be independent, uniformly distributed random variables on the interval [0, 0]. Find the a probability distribution function of Y() = max(Y₁; Y₂;; Y₁). b density function of y(n). c mean and variance of y(n).
13.THEORY OF DISTRIBUTIONS
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9.12 question attached
Let X be a random variable that follows the beta distribution. This random variable is continuous and is defined over the interval from 0 to 1. The probability density function is given by whereand are integers, whose values determine the shape of the probability density function. Because X varies between 0 and 1, we can think of X as the probability that some event (say) E occurs or the proportion of times an event occurs in some population. For example, E could denote the event that a critical part in a newly designed car will lead to a catastrophic failure in accidents at high speeds. The expected value (i.e., mean) of this random variable is []. That is, . The Excel commands for the beta random variable are =beta.dist(x,,,true,0,1) for the cumulative probability distribution, and =beta.dist(x,,,false,0,1) for the probability density function. (a) Now, think in Bayesian terms.…

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1.6 Let Y, and Y2 have the joint probability density function given by 0 < y1 < y2 <1 elsewhere 0, 1.6.1 What value of k makes f (y1,y2) a joint probability density function. ( 1.6.2 Determine the distribution of f2(y2). /