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Suppose that a random variableXhas the normal distributionwith meanμand precision\(\tau \). Show that the random variable\({\bf{Y = aX + b}}\;\left( {{\bf{a}} \ne {\bf{0}}} \right)\)has the normal distribution with mean+band precision\(\frac{\tau }{{{{\bf{a}}^{\bf{2}}}}}\).

Short Answer

Expert verified

Proved. The random variable \(Y = aX + b\;\left( {a \ne 0} \right)\) has a normal distribution with mean \(a\mu + b\) and precision \(\frac{\tau }{{{a^2}}}\).

Step by step solution

01

Given information

X is a random variable from the normal distribution with mean\(\mu \)and precision\(\tau \). Consider a new variable Y. \(Y = aX + b\).

02

describe the mean and variance of the new variable

As there can be concluded that if X is a random variable that follows a normal distribution with mean\(\mu \)and variance\({\sigma ^2}\)then the random variable\(Y = aX + b\;\left( {a \ne 0} \right)\)has the normal distribution with mean\(a\mu + b\)and variance\({a^2}{\sigma ^2}\).

As,

\(\begin{align}E\left( Y \right) &= aE\left( X \right) + E\left( b \right)\\ &= a\mu + b\end{align}\)

And

\(\begin{align}Var\left( Y \right) &= {a^2}Var\left( X \right) + 0\\ &= {a^2}{\sigma ^2}\end{align}\)

03

Calculate the precision

By the definition of precision, a precision of a normal distribution can be described as the reciprocal of the variance of the variable.

Therefore, for the random variable\(Y = aX + b\;\left( {a \ne 0} \right)\), the variance is\({a^2}{\sigma ^2}\). So, the reciprocal can be expressed as\(\frac{1}{{{a^2}{\sigma ^2}}}\).

Now for the X variable, the precision is\(\tau = \frac{1}{{{\sigma ^2}}}\)

So, the precision of Y is, \(\frac{\tau }{{{a^2}}}\)and the mean is \(a\mu + b\).

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Most popular questions from this chapter

Suppose thatXhas thetdistribution withmdegrees of freedom(m >2). Show that Var(X)=m/(m−2).

Hint:To evaluate\({\bf{E}}\left( {{{\bf{X}}^{\bf{2}}}} \right)\), restrict the integral to the positive half of the real line and change the variable fromxto

\({\bf{y = }}\frac{{\frac{{{{\bf{x}}^{\bf{2}}}}}{{\bf{m}}}}}{{{\bf{1 + }}\frac{{{{\bf{x}}^{\bf{2}}}}}{{\bf{m}}}}}\)

Compare the integral with the p.d.f. of a beta distribution. Alternatively, use Exercise 21 in Sec. 5.7.

Suppose a random variable has a normal distribution with a mean of 0 and an unknown standard deviation σ> 0. Find the Fisher information I (σ) in X.

Suppose that\({X_1},...,{X_n}\)form a random sample from the normal distribution with unknown mean μ and unknown variance\({\sigma ^2}\). Describe a method for constructing a confidence interval for\({\sigma ^2}\)with a specified confidence coefficient\(\gamma \left( {0 < \gamma < 1} \right)\) .

Question:Suppose that a random variable X has the Poisson distribution with unknown mean λ (λ > 0). Show that the only unbiased estimator of\({{\bf{e}}^{{\bf{ - 2\lambda }}}}\)is the estimator δ(X) such that δ(X) = 1 if X is an even integer and δ(X) = −1 if X is an odd integer.

In the situation of Exercise 9, suppose that a prior distribution is used forθwith p.d.f.ξ(θ)=0.1 exp(−0.1θ)forθ >0. (This is the exponential distribution with parameter 0.1.)

  1. Prove that the posterior p.d.f. ofθgiven the data observed in Exercise 9 is

\({\bf{\xi }}\left( {{\bf{\theta }}\left| {\bf{x}} \right.} \right){\bf{ = }}\left\{ \begin{align}{l}{\bf{4}}{\bf{.122exp}}\left( {{\bf{ - 0}}{\bf{.1\theta }}} \right)\;{\bf{if}}\;{\bf{4}}{\bf{.8 < \theta < 5}}{\bf{.2}}\\{\bf{0}}\;{\bf{otherwise}}\end{align} \right.\)

  1. Calculate the posterior probability \(\left| {{\bf{\theta - }}{{{\bf{\bar X}}}_{\bf{2}}}} \right|{\bf{ < 0}}{\bf{.1}}\), which \({{\bf{\bar X}}_{\bf{2}}}\)is the observed average of the datavalues.
  2. Calculate the posterior probability thatθis in the confidence interval found in part (a) of Exercise 9.
  3. Can you explain why the answer to part (b) is so close to the answer to part (e) of Exercise 9? Hint:Compare the posterior p.d.f. in part (a) to the function in Eq. (8.5.15).
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