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…

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?