We propose an effective methodology to compile functional programs to Bayesian networks. While in this talk we will mainly deal with the first order fragment, our technique can scale to the higher-order case, covering languages featuring recursion, and complex evaluation mechanisms, such as call-by-push-value. The main technical ingredient is the introduction of a parallel version of the Krivine Abstract Machine, that is able to build the Bayesian network while evaluating the program.