What is the difference between the Monte Carlo (MC) and Monte Carlo Markov Chain (MCMC) method? The goal of both methods seems to be to derive an estimate of a posterior/target distribution. If a process model exists which links some input parameters (which are themselves uncertain and can be described by a PDF) to an output parameter through a model equation or other computations, why would one choose one method over the other? Would both be applicable? Can one make a statement on the benefit of one method over the other with respect to the number of required draws/simulation runs in order to reach a sufficiently good approximation of the target PDF?
 A: In general Monte Carlo (MC) refers to estimating an integral by using random sampling to avoid curse of dimensionality problem. Also, once you have the samples, it's possible to compute the expectations of any random variable with respect to the sampled distribution.
A subclass of MC is MCMC you set up a Markov chain whose stationary distribution is the target distribution that you want to sample from. The main thing about many MCMC methods is that due to the fact that you've set up a Markov chain, the samples are positively correlated and thereby increases the variance of your integral/expectation estimates. The better situation is to make your samples independent (or have carefully constructed negative correlation) to reduce the variance. However, many distributions that you want to sample from are incredibly complicated objects and are difficult to sample from directly. Hence the construction and use of MCMC.
A: The short answer is: An MCMC is a MC, but not all MCs are MCMC. 
The slightly longer answer: MC methods are a class of methods, of which MCMC is one possibility. Even MCMC does not uniquely define your method as there are different variations of MCMC. 
You can read more in: Robert, C. P., & Casella, G. (2004). Monte Carlo statistical methods. New York: Springer.
