# Understanding Monte Carlo sampling

In rejection sampling or Markov chain Monte Carlo methods, we usually have a target distribution $p(x)$ whose form makes it difficult or impossible to draw samples directly, but we can evaluate $p(x)$ up to a normalising constant. We then sample from a simpler distribution which is proportional to $p(x)$.

My problem appears here: In order to accept/reject the proposed value $x'$, we evaluate the value of $p(x')$ and see if its within its bounds.

Why can we evaluate a value $p(x)$, but not sample directly from $p(.)$? It seems like if we can resolve $p(x)$ for all $x$, say uniformly sampled over the support of $p(.)$, we can directly assess the shape of $p(x)$ without any distribution convergence steps.

If someone could spot where I am confused, it would be very appreciated, as I don´t seem to find any clear explanation on this issue anywhere (must be really trivial..! )

• If your distribution has a very narrow peak, you might miss this peak with uniform sampling. – ziggystar Dec 15 '15 at 16:11
• You seem to be jumping between something that sounds like Gibbs sampling and something that sounds like Metropolis-Hastings, possibly with a little of something else thrown in. It sounds like you've read a bunch of things but have not done any MCMC and the ideas have become jumbled together in your head. Choose a particular thing to ask about (like MH), and try to ask a more specific question. – Glen_b Dec 17 '15 at 7:08
• @Glen_b You are definitely right with that assumption. Nevertheless I feel that Sobi helped with the main issues I had, specially with the important difference between sampling and evaluating. Hopefully the rest will start to clear after implementing MCMC methods in practice. – hirschme Dec 18 '15 at 12:32

I think what you have in mind is to evaluate $p(x), \forall x \in \Omega$ and then treat it as a discrete distribution and pick an outcome at random and according to the probabilities (which we know how to do for a discrete distribution).

The problem with that, however, is that $|\Omega|$ is often extremely large and in some cases infinite (if $x$ is continuous) making the above approach not practical.

Also, it is important to realize the distinction between being able to draw a sample from a distribution and being able to evaluate a distribution at an arbitrary outcome. The latter does not mean that you can sample from the distribution easily. As far as I know, we only know how to draw samples from a uniform distribution, and sampling from all other distributions (e.g. Gaussian, categorical, etc.) is done by applying various tricks on uniformly drawn samples (I might be wrong on this though).

The method is extensively used to sample from a posterior distribution in Bayesian statistics. The following refers to that situation.

Following Bayes' theorem, the posterior distribution is proportional to the prior times the likelihood. In notations:

$$p(\theta | x) \propto p(\theta) \times p(x | \theta)$$

Sometimes, $p(\theta | x)$ corresponds to a known distribution, like Student. In that case, values can directly be sampled from that distribution and MCMC is not needed.

But, $p(\theta | x)$ does not always correspond to a known distribution...

Given the prior and the likelihood, however, you can always resolve the posterior at every parameter value up to a multiplicative constant (cf. Bayes' theorem above). As explained by @Sobi, "Uniform sampling" (as you suggest) will not work for continuous distributions. But, the MCMC method applies.