Marginal distribution of normal random variable with a normal mean

Question

I have a question about calculation of conditional density of two normal distributions. I have random variables $X|M \sim \text{N}(M,\sigma^2)$ and $M \sim \text{N}(\theta, s^2)$, with conditional and marginal densities given by:

$$\begin{equation} \begin{aligned} f(x|m) &= \frac{1}{\sigma \sqrt{2\pi}} \cdot \exp \Big( -\frac{1}{2} \Big( \frac{x-m}{\sigma} \Big)^2 \Big), \\[10pt] f(m) &= \frac{1}{s \sqrt{2\pi}} \cdot \exp \Big( - \frac{1}{2} \Big( \frac{m-\theta}{s} \Big)^2 \Big). \end{aligned} \end{equation}$$

I would like to know the marginal distribution of $X$. I have multiplied the above densities to form the joint density, but I cannot successfully integrate the result to get the marginal density of interest. My intuition tells me that this is a normal distribution with different parameters, but I can't prove it.

Answer 1

Your intuition is correct - the marginal distribution of a normal random variable with a normal mean is indeed normal. To see this, we first re-frame the joint distribution as a product of normal densities by completing the square:

$$\begin{equation} \begin{aligned} f(x,m) &= f(x|m) f(m) \\[10pt] &= \frac{1}{2\pi \sigma s} \cdot \exp \Big( -\frac{1}{2} \Big[ \Big( \frac{x-m}{\sigma} \Big)^2 + \Big( \frac{m-\theta}{s} \Big)^2 \Big] \Big) \\[10pt] &= \frac{1}{2\pi \sigma s} \cdot \exp \Big( -\frac{1}{2} \Big[ \Big( \frac{1}{\sigma^2}+\frac{1}{s^2} \Big) m^2 -2 \Big( \frac{x}{\sigma^2} + \frac{\theta}{s^2} \Big) m + \Big( \frac{x^2}{\sigma^2} + \frac{\theta^2}{s^2} \Big) \Big] \Big) \\[10pt] &= \frac{1}{2\pi \sigma s} \cdot \exp \Big( -\frac{1}{2 \sigma^2 s^2} \Big[ (s^2+\sigma^2) m^2 -2 (x s^2+ \theta \sigma^2) m + (x^2 s^2+ \theta^2 \sigma^2) \Big] \Big) \\[10pt] &= \frac{1}{2\pi \sigma s} \cdot \exp \Big( - \frac{s^2+\sigma^2}{2 \sigma^2 s^2} \Big[ m^2 -2 \cdot \frac{x s^2 + \theta \sigma^2}{s^2+\sigma^2} \cdot m + \frac{x^2 s^2 + \theta^2 \sigma^2}{s^2+\sigma^2} \Big] \Big) \\[10pt] &= \frac{1}{2\pi \sigma s} \cdot \exp \Big( - \frac{s^2+\sigma^2}{2 \sigma^2 s^2} \Big( m - \frac{x s^2 + \theta \sigma^2}{s^2+\sigma^2} \Big)^2 \Big) \\[6pt] &\quad \quad \quad \text{ } \times \exp \Big( \frac{(x s^2 + \theta \sigma^2)^2}{2 \sigma^2 s^2 (s^2+\sigma^2)} - \frac{x^2 s^2 + \theta^2 \sigma^2}{2 \sigma^2 s^2} \Big) \\[10pt] &= \frac{1}{2\pi \sigma s} \cdot \exp \Big( - \frac{s^2+\sigma^2}{2 \sigma^2 s^2} \Big( m - \frac{x s^2 + \theta \sigma^2}{s^2+\sigma^2} \Big)^2 \Big) \cdot \exp \Big( -\frac{1}{2} \frac{(x-\theta)^2}{s^2+\sigma^2} \Big) \\[10pt] &= \sqrt{\frac{s^2+\sigma^2}{2\pi \sigma^2 s^2}} \cdot \exp \Big( - \frac{s^2+\sigma^2}{2 \sigma^2 s^2} \Big( m - \frac{x s^2 + \theta \sigma^2}{s^2+\sigma^2} \Big)^2 \Big) \\[6pt] &\quad \times \sqrt{\frac{1}{2\pi (s^2+\sigma^2)}} \cdot \exp \Big( -\frac{1}{2} \frac{(x-\theta)^2}{s^2+\sigma^2} \Big) \\[10pt] &= \text{N} \Big( m \Big| \frac{xs^2+\theta\sigma^2}{s^2+\sigma^2}, \frac{s^2 \sigma^2}{s^2+\sigma^2} \Big) \cdot \text{N}(x|\theta, s^2+\sigma^2). \end{aligned} \end{equation}$$

We then integrate out $m$ to obtain the marginal density $f(x) = \text{N}(x|\theta, s^2+\sigma^2)$. From this exercise we see that $X \sim \text{N}(\theta, s^2+\sigma^2)$.

Answer 2

Let $$X = m +\epsilon$$ where $m \sim N(\theta,s^2)$ and $\epsilon \sim N(0,\sigma^2)$ and they are independent.

Then $X|m$ and $m$ follows the distributions specified in the question.

$E(X)=E(m) = \theta$

$Var(X) = Var(m) +Var(\epsilon) = s^2+\sigma^2$

According to "The sum of random variables following Normal distribution follows Normal distribution", and the normal distribution is determined by mean and variance, we have

$$X \sim N(\theta, s^2+\sigma^2)$$

Answer 3

Here's a solution using moment generating functions, as suggested by @SecretAgentMan, that also ties in with the very slick answer provided by @user158565. If you like, you can view this as an (overly) rigorous justification of the decomposition provided by @user158565.

Let $M \sim N(\theta, s^2)$ and $X|M \sim N(M,\sigma^2)$. We are asked to find the unconditional distribution of $X$. To this end, define $\varepsilon \equiv X - M$. We show that $\varepsilon \sim N(0, \sigma^2)$ by calculating its moment generating function (mgf). We have \begin{align*} \mathbb{E}\left[e^{t\varepsilon}\right] &= \mathbb{E}\left[\exp\left\{t(X-M)\right\}\right] = \mathbb{E}\left[\mathbb{E}\left(\left. e^{tX}e^{-tM}\right|M\right) \right]\\ &= \mathbb{E}\left[e^{-tM}\mathbb{E}\left(\left. e^{tX}\right|M\right) \right] = \mathbb{E}\left[ e^{-tM} \exp\left\{tM + \frac{1}{2}\sigma^2 t^2 \right\}\right]\\ &= \mathbb{E}\left[ \exp\left\{\frac{1}{2}\sigma^2 t^2 \right\}\right] \end{align*} using iterated expectations and the fact that $X|M$ is a normal random variable with mean $M$ and variance $\sigma^2$, so that its moment generating function is $\exp\left\{ tM + \frac{1}{2}\sigma^2 t^2 \right\}$. We recognize $\mathbb{E}[e^{t\varepsilon}]$ as the mgf of a normal random variable, hence $\varepsilon \sim N(0,\sigma^2)$. Next, we show that $M$ and $\varepsilon$ are independent by showing that their joint mgf equals the product of the respective marginal mgfs. Again using iterated expectations and the mgf of $X|M$, we have \begin{align*} \mathbb{E}\left[ \exp\left\{ t_1 M + t_2 \varepsilon \right\} \right] &= \mathbb{E}\left[ \exp\left\{ t_1 M + t_2(X -M) \right\} \right] = \mathbb{E}\left[ \exp\left\{(t_1 - t_2)M + t_2 X\right\} \right]\\ &= \mathbb{E}\left[ \exp\left\{(t_1 - t_2)M\right\} \mathbb{E}\left(\left. e^{t_2 X}\right|M \right) \right]\\ &= \mathbb{E}\left[ \exp\left\{(t_1 - t_2)M\right\} \exp\left\{ t_2 M + \frac{1}{2}\sigma^2 t_2^2 \right\} \right]\\ &= \mathbb{E}\left[ \exp\left\{ t_1 M + \frac{1}{2}\sigma^2 t_2^2 \right\} \right]\\ &= \mathbb{E}\left[e^{t_1 M}\right] \exp\left\{\frac{1}{2}\sigma^2 t_2^2 \right\} \\ &= \mathbb{E}\left[e^{t_1 M}\right] \mathbb{E}\left[ e^{t_2 \varepsilon} \right] \end{align*} as claimed. We have shown that $X = M + \varepsilon$ where $\varepsilon \sim N(0, \sigma^2)$ independently of $M \sim N(\theta, s^2)$. It follows that $X \sim N(\theta, s^2 + \sigma^2)$.

Answer 4

Finally I found a easier solution of my problem by the MGF, without to much calculations.

Let assume $$X_1, X_2, ... X_n $$ which follow a specific law

The MGF of this variables is $$ M(t_1,t_2,...,t_n) = E[\exp(t_1X_1 + t_2X_2 + ... + t_nX_n)] $$

In a similar way we can define moments in a conditional approach like this:

$$ E[X|M]=∫xf(x|M) dx. $$

Using this, we can prove that: $$ X \sim N(\theta, s^2+ \sigma^2)$$