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Suppose that two random variables\({\bf{\mu }}\,\,{\bf{and}}\,\,{\bf{\tau }}\)have the joint normal-gamma distribution with hyperparameters\({{\bf{\mu }}_{\bf{0}}}{\bf{ = 4,}}{{\bf{\lambda }}_{\bf{0}}}{\bf{ = 0}}{\bf{.5,}}{{\bf{\alpha }}_{\bf{0}}}{\bf{ = 1,}}{{\bf{\beta }}_{\bf{0}}}{\bf{ = 8}}\)Find the values of (a)\({\bf{Pr}}\left( {{\bf{\mu > 0}}} \right)\)and (b)\({\bf{Pr}}\left( {{\bf{0}}{\bf{.736 < \mu < 15}}{\bf{.680}}} \right)\).

Short Answer

Expert verified

(a) the answer is 0.78868

(b) the answer is 0.25005

Step by step solution

01

Given information

It is given that two variables \(\mu \,\,and\,\,\tau \)have the joint normal-gamma distribution such that \({\mu _0} = 4,{\lambda _0} = 0.5,{\alpha _0} = 1,{\beta _0} = 8\)

02

Define Normal-Gamma distribution

Let \(\mu \,\,{\rm{and}}\,\,\tau \) be random variables. Suppose that the conditional distribution of \(\mu \,\,{\rm{given}}\,\,\tau \) is the normal distribution with mean \({\mu _0}\) and precision \({\lambda _0}\tau \) .

Suppose also that the marginal distribution of \(\,\tau \) is the gamma distribution with parameters \({\alpha _0}\,\,{\rm{and}}\,\,{\beta _0}\).

Then we say that the joint distribution of\(\mu \,\,and\,\,\tau \) is the normal-gamma distribution with hyperparameters \({\mu _0},{\lambda _0},{\alpha _0},{\beta _0}\).

03

Define a new variable

Let,

\(\begin{align}U &= {\left( {\frac{{{\lambda _0}{\alpha _0}}}{{{\beta _0}}}} \right)^{\frac{1}{2}}}\left( {\mu - {\mu _0}} \right)\\ &= {\left( {\frac{{0.5 \times 1}}{8}} \right)^{\frac{1}{2}}}\left( {\mu - 4} \right)\\ &= 0.25\left( {\mu - 4} \right)\end{align}\)

Here U follows t distribution with \(2{\alpha _0}\) degrees of freedom, that is 2.

04

(a) Find the confidence interval

\(\begin{align}\Pr \left( {\mu > 0} \right)\\ &= \Pr \left( {\left( {\mu - 4} \right) \times 0.25 > - 4 \times 0.25} \right)\\ &= \Pr \left( {U > - 1} \right)\\ &= {\rm{0}}{\rm{.78868}}\end{align}\)

Therefore, the answer is 0.78868

05

(b) Find the confidence interval

\(\begin{align}\Pr \left( {0.736 < \mu < 15.680} \right)\\ &= \Pr \left( {\left( {0.736 - 4} \right) \times 0.25 < \left( {\mu - 4} \right) \times 0.25 < \left( {15.680 - 4} \right) \times 0.25} \right)\\ &= \Pr \left( { - 0.816 < U < 2.92} \right)\\ &= 0.99994 - 0.74989\\ &= 0.25005\end{align}\)

Therefore, the answer is 0.25005

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Most popular questions from this chapter

Question:Suppose that\({{\bf{X}}_{\bf{1}}}{\bf{,}}...{\bf{,}}{{\bf{X}}_{\bf{n}}}\)form a random sample from the uniform distribution on the interval (0, θ), where the value of the parameter θ is unknown; and let\({{\bf{Y}}_{\bf{n}}}{\bf{ = max}}\left( {{{\bf{X}}_{\bf{1}}}{\bf{,}}...{{\bf{X}}_{\bf{n}}}} \right)\). Show that\(\left( {\frac{{\left( {{\bf{n + 1}}} \right)}}{{\bf{n}}}} \right){{\bf{Y}}_{\bf{n}}}\) is an unbiased estimator of θ.

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