10. The system of claim 9 , wherein the system memory comprises code configured to test the learner object with the sampler object by computing synthetic data for the dyadic Bayesian model and using the synthetic data to judge an accuracy of the posterior distribution or the posterior predictive distribution.
9. A system for modeling a data generating process, comprising: a processor that is adapted to execute stored instructions; and a system memory, wherein the system memory comprises code configured to: generate a dyadic Bayesian model comprising a pair of probabilistic functions representing a prior distribution and a sampling distribution, a sampler object, and a learner object; train the dyadic Bayesian model based on observed data with the learner object to produce a trained dyadic Bayesian model; generate a posterior distribution over parameters based on the trained dyadic Bayesian model; generate a posterior predictive distribution based on the posterior distribution; and predict an outcome of observable variables using the posterior predictive distribution.