Sampling of a Gaussian posterior Sorry for my simple doubt, but I'm quite newbie and don't clearly get how to sample the posterior Bayesian distribution. My likelihood and prior are normal and I know how to calculate the posterior. How do I sample now? Can someone explain it in the simplest way?

EDIT:
Bayes theorem states: 
$$
p(θ|y)=\frac{p(y|θ)p(θ)}{p(y)}
$$


*

*When I am sampling, what example do I sample for the posterior?

*I read that the rnorm command (in R) can easily sample; I gather the syntax would be: rnorm(n samples, mean, std). Is this kind of sampling as valid as MCMC? What is it executing there exactly?

*Is sampling from a normal posterior equal to sampling from an isolated normal distribution?
 A: 
I read that the rnorm command (in R) can easily sample; I gather the
  syntax would be: rnorm(n samples, mean, std). Is this kind of sampling
  as valid as MCMC? What is it executing there exactly?

In principle, sampling form a posterior distribution is the same as sampling from any other distribution. If the distribution is of a known form (like a Gaussian, Gamma, Inverse Gamma, Beta etc), then you can use inbuilt functions in R to sample from these. 
These functions like rnorm use iid sampling techniques, and not MCMC. This is a good thing. MCMC produces correlated samples that are not identically distributed. MCMC is used when iid sampling techniques fail. Unfortunately, in many Bayesian settings, the posterior distribution is not a known distribution, and of a complicated form. Sampling from such a distribution often requires MCMC.
However, known posterior distributions are sampled from using inverse CDF, variable transformation or Rejection sampling techniques which provide iid samples. I am not sure what rnorm uses, but one of the most common ways of sampling from a Normal distribution is the Box-Mueller transformation. 
So in short, if your posterior distribution is of a known form, and there exists a function to sample from it, go ahead and use that function.
