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I have found a reference.

The Jeffreys prior in the normal linear regression model is:

$$\pi(\beta,\sigma^2)\propto \dfrac{1}{(\sigma^2)^\frac{p+2}{2}}.$$

The reference is: http://www.jstor.org/stable/2290514 (see text just after Eq. 2.9 -- a bit hard to read!)

I have found a reference.

The Jeffreys prior in the normal linear regression model is:

$$\pi(\beta,\sigma^2)\propto \dfrac{1}{(\sigma^2)^\frac{p+2}{4}},$$

or more commonly in the $\sigma$ parameterization:

$$\pi(\beta,\sigma)\propto \dfrac{1}{\sigma^\frac{p+2}{2}}.$$

The reference is: http://www.jstor.org/stable/2290514 (see text just after Eq. 2.9 -- a bit hard to read!)

I have found a reference.

The Jeffreys prior in the normal linear regression model is:

$$\pi(\beta,\sigma^2)\propto \dfrac{1}{(\sigma^2)^\frac{p+2}{2}}.$$

The reference is: http://www.jstor.org/stable/2290514

I have found a reference.

The Jeffreys prior in the normal linear regression model is:

$$\pi(\beta,\sigma^2)\propto \dfrac{1}{(\sigma^2)^\frac{p+2}{2}}.$$

The reference is: http://www.jstor.org/stable/2290514 (see text just after Eq. 2.9 -- a bit hard to read!)

I have found a reference.

The answer is: $$\pi(\beta,\sigma^2)\propto \dfrac{1}{(\sigma^2)^\frac{p−2}{2}}$$.

The reference is: http://www.jstor.org/stable/2290514

I have found a reference.

The Jeffreys prior in the normal linear regression model is:

$$\pi(\beta,\sigma^2)\propto \dfrac{1}{(\sigma^2)^\frac{p+2}{2}}.$$

The reference is: http://www.jstor.org/stable/2290514