Let X₁,..., Xn be i.i.d. Normal (0,0²). (a) Suppose that is a priori distributed normal with mean a and variance r2. Determine the Bayes estimator of 8 based from squared loss function. Compare its MSE with the mle of 0. , a is (b) Assume now that has a double exponential distribution, that is, p(0) known. Find the mean of the posterior distribution of e 2a -exp

MATLAB: An Introduction with Applications
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Let X1,.., Xn be i.i.d. Normal(theta,sigma^2).

(a) Suppose that theta is a priori distributed normal with mean mu and variance r^2. Determine the Bayes estimator of theta based from squared loss function. Compare its mean squared errpor MSE with the maximum likelihood estimator mle of theta.

(b) Assume now that has a double exponential distribution, that is, p(theta)= 1/(2a) * exp (-|theta|/a), a is known. Find the mean of the posterior distribution of theta.

Let X1,..X, be i.i.d. Normal(, o?).
(a) Suppose that e is a priori distributed normal with mean u and variance r. Determine the
Bayes estimator of 6 based from squared loss function. Compare its MSE with the mle of 0.
(b) Assume now that 0 has a double exponential distribution, that is, p(@)
known. Find the mean of the posterior distribution of @
es (-), a is
%3D
exp
Transcribed Image Text:Let X1,..X, be i.i.d. Normal(, o?). (a) Suppose that e is a priori distributed normal with mean u and variance r. Determine the Bayes estimator of 6 based from squared loss function. Compare its MSE with the mle of 0. (b) Assume now that 0 has a double exponential distribution, that is, p(@) known. Find the mean of the posterior distribution of @ es (-), a is %3D exp
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