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.
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. 14. The system of claim 9 , wherein the posterior predictive distribution of the data set comprises a distribution of output data within the data set based on input data within the data set.