(following Hogg, McKean & Craig, exercise 11.2.2) Let X1, X2, .., X10 be a random sample from a gamma distribution with a = 3 and ß = 1/0. Suppose we believe that 0 follows a gamma-distribution with a = 3 and B = 2: a) Find the posterior distribution of 0. b) If the observed x = 16.2, what is the Bayes point estimate associated with the square-error loss function? c) What is the Bayes point estimate using the mode of the posterior distribution?
(following Hogg, McKean & Craig, exercise 11.2.2) Let X1, X2, .., X10 be a random sample from a gamma distribution with a = 3 and ß = 1/0. Suppose we believe that 0 follows a gamma-distribution with a = 3 and B = 2: a) Find the posterior distribution of 0. b) If the observed x = 16.2, what is the Bayes point estimate associated with the square-error loss function? c) What is the Bayes point estimate using the mode of the posterior distribution?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Problem 1P
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In question https://www.bartleby.com/questions-and-answers/following-hogg-mckean-and-craig-exercise-11.2.2-let-x1-x2-..-x10-be-a-random-sample-from-a-gamma-dis/143478e2-92eb-4ddf-b9b4-e0ca3713f989?v=2&mrasn=800265.994143.YfjU3Gao, the solution has "gamma" parameters.
Question: Where do the "gamma" parameters, found in the solution, come from?

Transcribed Image Text:(following Hogg, McKean & Craig, exercise 11.2.2)
Let X1, X2, .., X10 be a random sample from a gamma distribution with a = 3 and ß = 1/0. Suppose we believe
that 0 follows a gamma-distribution with a = 3 and B = 2:
a) Find the posterior distribution of 8.
b) If the observed x = 16.2, what is the Bayes point estimate associated with the square-error loss
function?
c) What is the Bayes point estimate using the mode of the posterior distribution?

Transcribed Image Text:AnsueT:-
f(dotole)f(0)
fo f(datale )Ple)
o) P(oldata)-
I(3)23
e3- le -0-58
Y(3)23
F.
N Gramma (a = an+3, B= Expt05)
%3D
So, Postesor dt & as given above
b) undeo sa, e loss , Bayes estimate s the posteror
mean =
= 33, (4I10*B.2+0.5)) = 0.1808
C) Mode e C -1)*B = (3n+2) * (/10* 18.2 +0.5)= 0.14534
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