You’ve built a model that has ten different variables with complicated independence relationships
between them, and both continuous and discrete variables that have complicated, multi-parameter
distributions.
Computing the joint probability distribution is complex, but it turns out that computing the
conditional probabilities for the variables is easy. What is the most computationally efficient for

computing the expected value?

A.
Method of moments

B.
Markov Chain Monte Carlo

C.
Gibbs sampling

D.
Numerical quadrature