Do prior hyperparameters update as you take successive measurements in the case of Gaussian unknown mean? I am trying to use conjugate priors to estimate the mean $\mu$ of a Gaussian with known variance, $\sigma^2$. Derived was that the choice of prior should be:
$p(\mu) = N(\mu | \mu_0, \sigma_0^2)$
Following through with this, I come up with an estimate for $\mu$:
$\mu_N = \frac{\sigma^2\mu_0}{N\sigma_0^2+\sigma^2} + \frac{N\sigma_0^2\mu_{ML}}{N\sigma_0^2+\sigma^2}$
In this case, $\mu_{ML} = \frac{1}{N}\sum x_i$, which I have. 
Now my question is, I did another example fairly similar concerning the Bernoulli distribution, where the hyperparameters 'updated' with each successive measurement. The notation of the references I've been using is a little bit shady on this but I'm wondering if the same applies here. In particular, the posterior of a previous element yields the new hyperparameters for the prior of the next measurement. 
In this one, though, it seems like you just pick hyperparameters and stick with them, $\mu_0, \sigma_0^2$. There doesn't seem to be any indication that the next prior has a different $\mu_0$ than the previous one. Is this the case? Am I skipping that step and just using the $\mu_N$ equation to sequentially estimate $\mu$, or are the hyperparameters in fact changing with each new measurement taken? 
 A: I've checked with some sources and done some stuff in MATLAB to confirm results. Updating the prior parameters regularly seems to give better results, but it costs more computationally and keeping a static $\mu_0$ works just as well if not better. In the case of smaller values of N, there is preference given to the prior, for larger N the preference is given to the $\mu_{ML}$. Even if I was to keep changing the prior then, updating it regularly, for large N it would be meaningless, since the result converges to $\mu_{ML}$. So for large N, which is the case for me, the result is mostly the same. For smaller N, regularly changing $\mu_0$ is something you can consider, I suppose. Not sure how correct that is.
A: In the prior the hyperparameters are $\mu_0$ and $\sigma_0^2$. Given that you've got the correct $\mu_N$ you've probably already done most of the following:
We have the prior
$$p(\mu)\propto \exp\left(\frac{(\mu-\mu_0)^2}{2\sigma_0^2}\right)$$
Given we observe $(x_1,\dots,x_N)$ the likelihood is
$$L(x|\mu)\propto\prod_{i=1}^N\exp\left(\frac{(\mu-x_i)^2}{2\sigma^2}\right)=\exp\left(\sum_{i=1}^N\frac{(\mu-x_i)^2}{2\sigma^2}\right)$$
So by Bayes
$$\begin{align*}
p(\mu|x)&\propto \exp\left(\frac{(\mu-\mu_0)^2}{2\sigma_0^2}\right) \exp\left(\sum_{i=1}^N\frac{(\mu-x_i)^2}{2\sigma^2}\right)\\
&\propto\dots\\
&\propto\exp\left(-\frac{1}{2}\left(\left(\frac{1}{\sigma_0^2}+\frac{N}{\sigma^2}\right)\mu^2-2\left(\frac{\mu_0}{\sigma_0^2}+\frac{N\mu_{ML}}{\sigma^2}\right)\mu\right)\right)\\
&\propto\exp\left(-\frac{1}{2}\left(\frac{1}{\sigma_0^2}+\frac{N}{\sigma^2}\right)\left(\mu^2-2\mu_N\mu\right)\right)\\
\end{align*}$$
where $\mu_N$ is the estimate you already worked out. Completing the square in this expression for the posterior gives
$$p(\mu|x)\propto\exp\left(-\frac{1}{2}\left(\frac{1}{\sigma_0^2}+\frac{N}{\sigma^2}\right)\left(\mu-\mu_N\right)^2\right)$$
This is a normal with mean $\mu_N$ and variance $$\left(\frac{1}{\sigma_0^2}+\frac{N}{\sigma^2}\right)^{-1}.$$
So you should use the new hyperparameters $\mu_N$ and $\left(\frac{1}{\sigma_0^2}+\frac{N}{\sigma^2}\right)^{-1}$.
