How does the posterior of a dirac prior look like? Edit for the Moderators: Should this question migrate to stats.stackexchange?
I have a very basic question concerning updating from a prior to a posterior in bayesian statistics.

Setting:


*

*I enter in a casino and I have a degenerate prior over an american roulette wheel, say $\delta_{\{0\}}$.

*The outcome of the first roll I witness is not $0$. 
[Notation: $\delta_{\{0\}}$ denotes the dirac measure associated with the belief that the roulette is unfair and completely bias towards $0$]

Questions: 


*

*How do I update my degenerate prior?

*Is my posterior the uniform over $\{ 0, 1 , \dots , 36 \}$?.

*What kind of prior is it?
To what kind of posterior does it lead?
[I assume there are some technical names for these things, that I am unaware of]

Of course the questions come from the fact that it is not possible to update anything with such a prior, if the hard evidence I can obtain does not support it.
Any feedback is most welcome.
Thank you for your time.

*

*Using Bayes' rule, for each value $x$ you have
$$P(x | data) \propto P(data | x) prior(x)$$
Therefore, $P(x|data)$ is zero if $x \neq 0$ and so $P(0|data) = 1$, so your posterior is the same as the prior.

*No, the posterior is the same as the prior. Using this prior says that you have decided that the answer is zero and no evidence can convince you otherwise.

*This could be called a point prior or Dirac prior. It should never be used because one of the basic principles of statistics is that a prior should not assign zero probability to anything. This is called Cromwell's Principle.
Edit: another name for a mixture of a Dirac prior and a uniform distirbution is spike and slab prior. You can find out more about it by searching for this term.
