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Let X₁, ...., X₁; i = 1,2, ..., n is a random sample from the population with the probability mass function: f (x|0) = 0 (1 - 0)* ; x = 0,1,2,... 0 <0 < 1 if it is known that parameter has a prior beta probability function (3;4) as follows: Γ(7) h(0) 0² (1-0)³ ; 0 <0 <1 Г(3)Г(4) 1. determine the likelihood function L(x|0) 2. find the density function with X and 0, i.e. g(x|0) 3. find the posterior distribution for 0, i.e. k(0|x) 4. find the Bayesian estimator for 0, that is, T =

Let X₁, ...., X₁; i = 1,2, ..., n is a random sample from the population with the probability mass function: f (x|0) = 0 (1 - 0)* ; x = 0,1,2,... 0 <0 < 1 if it is known that parameter has a prior beta probability function (3;4) as follows: Γ(7) h(0) 0² (1-0)³ ; 0 <0 <1 Г(3)Г(4) 1. determine the likelihood function L(x|0) 2. find the density function with X and 0, i.e. g(x|0) 3. find the posterior distribution for 0, i.e. k(0|x) 4. find the Bayesian estimator for 0, that is, T =

MATLAB: An Introduction with Applications
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 X₁, ...., Xn; i = 1,2,..., n is a random sample from the population with the probability mass function: f (x|0) = 0 (1 - 0)* ; x = 0,1,2,... 0 <0 < 1 if it is known that parameter 0 has a prior beta probability function (3;4) as follows: T(7) h(0) = T(3)r (4) 1. determine the likelihood function L(x|0) 2. find the density function with X and 0, i.e. g(x|8) 3. find the posterior distribution for 0, i.e. k(0|x) 4. find the Bayesian estimator for 0, that is, T 0² (1-0)³ ; 0 <0 <1
Transcribed Image Text:Let X₁, ...., Xn; i = 1,2,..., n is a random sample from the population with the probability mass function: f (x|0) = 0 (1 - 0)* ; x = 0,1,2,... 0 <0 < 1 if it is known that parameter 0 has a prior beta probability function (3;4) as follows: T(7) h(0) = T(3)r (4) 1. determine the likelihood function L(x|0) 2. find the density function with X and 0, i.e. g(x|8) 3. find the posterior distribution for 0, i.e. k(0|x) 4. find the Bayesian estimator for 0, that is, T 0² (1-0)³ ; 0 <0 <1
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