# Gaussian likelihood + which prior = Gaussian Marginal?

Given a Gaussian likelihood for a sample $y$ like $$p(y|\theta) = \mathcal{N}(y;\mu(\theta),\Sigma(\theta))$$ with $\Theta$ being the parameter space and $\mu(\theta)$, $\Sigma(\theta)$ arbitrary parameterisations of the mean vector and the covariance matrix.

Is it possible to specify a prior density $p(\theta)$ and parameterisation of the mean vector $\mu(\theta)$ and the covariance matrix $\Sigma(\theta)$ such that the marginal likelihood $$p(y)=\int_{\theta\in\Theta}N(y;\mu(\theta),\Sigma(\theta))p(\theta)d\theta$$ is a Gaussian likelihood?

I guess excluding the trivial solution that the covariance is known, that is, $\Sigma(\theta)=\Sigma$, where $\Sigma$ is an arbitrary fixed covariance matrix, this is not possible.

For the special case $\mu(\sigma^2)=\mu$ and $\Sigma(\sigma^2)=\sigma^2$, that is $y$ is one-dimensional, and $p(\sigma^2)=\mathcal{U}(\sigma^2;a,b)$, where $\mathcal{U}(\sigma^2;a,b)$ denotes the uniform density I can show it: \begin{align} p(y)&=\int_0^\infty \mathcal{N}(y;\mu,\sigma^2)\mathcal{U}(\sigma^2;a,b)d\sigma^2 \\ &= \frac{1}{b-a} \underbrace{\int_a^b \mathcal{N}(y;\mu,\sigma^2)}_\text{not a Gaussian density} \end{align}

The accepted answer contains a formal or informal proof or pointers to it.

## 3 Answers

Your conjecture seems to be true: only a constant variance can lead to a normal margin. My proof is limited to the case where the expectation $\boldsymbol{\mu}$ is known, and hence can be assumed to be zero. For the general case, more sophisticated arguments from functional analysis seem to be required.

Note that the question is actually about continuous mixture of normals as well as about Bayes. The statement proved here it that a (continuous) scale mixture of normals can be normal only for a trivial mixture.

First consider the case of a one-dimensional normal with known mean $\mu = 0$ and precision parameter $\omega := 1 / \Sigma >0$. Without loss of generality, we can assume that the parameter $\boldsymbol{\theta}$ is the precision $\omega$ itself. If the marginal distribution of $y$ is normal, then $\int \exp\{-y^2 \omega / 2\}\,\omega^{1/2} p(\omega)\,\text{d}\omega$ is a normal density up to a multiplicative constant. This density being an even function of $y$ must take the form $c\exp\{ -y^2 \omega_0 / 2\}$ for some $\omega_0 >0$ and some constant $c >0$. Since this holds for any $y$ we get with $s := y^2$ $$\int_0^\infty \exp\{-s \omega \,/ 2\}\,\omega^{1/2} p(\omega)\text{d}\omega = c \exp\{ -s \omega_0 \,/ 2\}$$ for all $s \geq 0$, which shows that the finite measure with density function $\omega \mapsto \omega^{1/2} p(\omega)$ is proportional to the Dirac mass at $\omega_0$ because these two measures have the same Laplace transform, up to a multiplicative constant. Thus $\omega$ is almost surely (a.s.) equal to $\omega_0$.

This proof extends to the $d$-dimensional normal with mean zero and precision matrix $\boldsymbol{\Omega}:=\boldsymbol{\Sigma}^{-1}$. The margin then writes as $\propto \int \exp\{-\mathbf{y}^\top \boldsymbol{\Omega}\,\mathbf{y} \,/ 2\}\, \left|\boldsymbol{\Omega}\right|^{1/2}p(\boldsymbol{\Omega})\,\text{d}\boldsymbol{\Omega}$ where the integral is on the set $\mathcal{P}$ of positive definite symmetric $d \times d$ matrices. If this integral is identical to $c\exp\{ -\mathbf{y}^\top \boldsymbol{\Omega}_0 \mathbf{y} / 2\}$, then by taking $\mathbf{y}:= \sqrt{s} \,\boldsymbol{u}$ for a scalar $s \geq 0$ and an arbitrary vector $\mathbf{u}$, we find as above that $\mathbf{u}^\top \boldsymbol{\Omega}\, \mathbf{u}$ must be a.s. equal to $\mathbf{u}^\top \boldsymbol{\Omega}_0 \mathbf{u}$, which shows that $\boldsymbol{\Omega}$ is a.s. equal to $\boldsymbol{\Omega}_0$. The proof works even if the measure conveniently written as having density $|\boldsymbol{\Omega}|^{1/2} p(\boldsymbol{\Omega})$ concentrates on a subset of $\mathcal{P}$ with Lebesgue measure zero, because the Laplace transform argument still applies. So the proof works for a general parameterisation of the precision (or variance) matrix.

• I have not understood your proof fully yet. In comparison to Jacky1's answer it seems relatively complex. What do you think about his proof? – Julian Karch Feb 25 '16 at 14:02
• Well I could not understand how the prior can depend of $y$ in Jacky's answer. Yet his statement as I understand it is wrong - I did the same error first:) Indeed, $\mu$ is not necessarily constant and if the variance is constant, $\mu$ still can be normal which is easily checked by completing a square. I now have a proof for the independent prior case (one-dimensional for simplicity), and hope to write it down soon, maybe as a new answer. The variance $\Sigma$ must be constant and $\mu$ must be normal (possibly degenerate). – Yves Feb 25 '16 at 15:31
• Could you expand your reasoning in the paragraph starting with "Since this holds for any $y$ and ending with "(a.s.) equal to $ω_0$"? Maybe with pointers to the theorems you are using? – Julian Karch Mar 7 '16 at 12:40

Assume that $\mu$ and $\Sigma$ are a priori independent and that $y$ has a normal margin with mean $\mu_0$ and variance $\Sigma_0$. I will prove that then the variance $\Sigma$ must be constant, and the mean $\mu$ must have a normal prior (possibly degenerate).

I will stick to the one-dimensional case for simplicity, using the characteristic function (c.f.) of $y$, i.e. $\phi_y(t) := \mathbb{E}[e^{yit}]$. We know that $\phi_y(t) = \exp\{\mu_0 it - \Sigma_0 t^2 /2$} and a similar formula holds for the distribution of $y$ conditional on $\mu$ and $\Sigma$, which is normal by assumption. So for any real $t$ $$\mathbb{E}[e^{yit}] = \int \mathbb{E}\left[e^{yit} \, \vert \,\mu,\,\Sigma\right]\, p(\mu) p(\Sigma) \,\text{d}\mu \text{d} \Sigma = \int \exp\left\{ \mu it - \Sigma t^2/2 \right\} \,p(\mu) p(\Sigma) \,\text{d}\mu \text{d}\Sigma,$$ and by rearranging the integral, we must have $$\exp\left\{ \mu_0 it - \Sigma_0 t^2 /2 \right\} = \left[\int \exp\left\{ \mu it \right\} p(\mu) \,\text{d}\mu \right] \left[\int \exp\left\{ -\Sigma t^2/2\right\} p(\Sigma) \,\text{d}\Sigma \right].$$ The assumptions needed for such a rearrangement are easily checked.

The first integral at right hand side, say $\phi_1(t)$, is the c.f. of $\mu$. Note that since $\phi_1(t) e^{-\mu_0 it}$ is found to be real, we see that the distribution of $\mu$ is symmetric w.r.t. $\mu_0$, and hence that $\mathbb{E}[\mu] = \mu_0$, as it might have been anticipated.

Now it turns out that the second integral at right hand side, say $\phi_2(t)$, is also a c.f. To see that, we must check that $\phi_2(0) = 1$, that $\phi_2$ is continuous at $t=0$ and also that the function $\phi_2$ is positive definite (p.d.). The first requirement is obvious, the second is proved by dominated convergence. Now turn to the p.d. requirement: if the prior distribution written as $p(\Sigma)\text{d}\Sigma$ is a Dirac mass, then $\phi_2$ is p.d. because $\phi_2$ is then the c.f. of a normal distribution. If the prior is a discrete mixture of Dirac masses, this is true as well since $\phi_2$ then is the c.f. of a mixture of normals. By a continuity argument, we see that $\phi_2$ is p.d.

Now let us use the powerful Lévy-Cramér theorem which tells that both functions $\phi_j$ for $j=1$, $2$ must take the form $\exp\{a_j i t - b_jt^2 /2 \}$ with $a_j$ real and $b_j \geq 0$. So $\mu$ must be normal (possibly degenerate) with mean $a_1 = \mu_0$. By simple algebra we then have $$\exp\{ -(\Sigma_0 - b_1) t^2 /2 \} = \int_0^\infty \exp\{ - \Sigma t^2 /2\} p(\Sigma) \, \text{d} \Sigma$$ which holds for any real $t$. Since any non-negative real writes as $t^2/2$, we see that the Laplace transform of the prior of $\Sigma$ must be equal to that of the Dirac mass at $\Sigma_0 - b_1$ and we are done.

• Thanks for your effort. It will take me some time to understand this. – Julian Karch Mar 2 '16 at 18:00

I have a proposition of proof for you, but you need to check it.

Assume that the marginal likelihood is Gaussian :

$p(y)=\mathcal{N}(y,m,\Gamma)$

then the prior density can be defined by

$p(\theta)=\mathcal{N}(y,\mu(\theta),\Sigma(\theta))^{-1}\mathcal{N}(y,m,\Gamma)f(\theta)$

where $f$ checks $\int_{\theta\in\Theta}f(\theta)d\theta =1$ and $f(\theta)\geq 0$ for $\theta\in\Theta$. ($f(\theta)$ is $p(\theta|y)$).

To be a density, the integral of the prior density $p(\theta)$ on $\Theta$ has to be equal to 1. In other words,

$\int_{\theta\in\Theta}\mathcal{N}(y,\mu(\theta),\Sigma(\theta))^{-1}\mathcal{N}(y,m,\Gamma)f(\theta)d\theta =1$.

It leads to

$\int_{\theta\in\Theta}\mathcal{N}(y,\mu(\theta),\Sigma(\theta))^{-1}\mathcal{N}(y,m,\Gamma)f(\theta)d\theta = \int_{\theta\in\Theta}f(\theta)d\theta$

This equality being true if and only if $\mu(\theta)=m$ and $\Sigma(\theta)=\Gamma$.

• I like the proof idea. I am pretty certain that all steps but the last one are valid. Surely the integral of two functions are the same if the functions are the same but this is not a necessary condition. Are you using a different theorem there? – Julian Karch Feb 24 '16 at 14:43
• If you replace $p(\theta|y)$ with its defintion via bayes in your first formula for $p(\theta)$, then it becomes $p(\theta)=p(\theta)$. Surely, nothing follows from this inequality. – Julian Karch Mar 3 '16 at 16:33