# 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: $$p(θ|y)=\frac{p(y|θ)p(θ)}{p(y)}$$

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

2. 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?

3. Is sampling from a normal posterior equal to sampling from an isolated normal distribution?

• Welcome to our site! It isn't really clear what you're asking here at the moment, I think you are going to have to be rather more specific. If you are just looking for R code, note that questions asking how something is done in a particular language or software package are off-topic here - have a look at our help center. – Silverfish Apr 16 '16 at 21:23
• I agree with @Silverfish. This question looks about R code which is more suitable for Stack Overflow. The posterior that you have obtained should also be a Normal distribution, so you can use rnorm in R. – Greenparker Apr 16 '16 at 21:31
• @Greenparker Without a reproducible example, I don't think the question in its current state is well-suited for SO either. – Silverfish Apr 16 '16 at 21:37
• I can imagine this having enough statistical content to be on topic here. Can you say more about your situation, your data & your model? At present, I'm not sure if there's much here that can be answered. – gung - Reinstate Monica Apr 16 '16 at 22:09
• I think this has enough statistical content to be reopened now. – gung - Reinstate Monica Apr 17 '16 at 13:18

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.