1. A method for modeling a data generating process, comprising: generating a dyadic Bayesian model comprising a pair of probabilistic functions representing a prior distribution and a sampling distribution; and modeling a data generating process based on the dyadic Bayesian model using observed data, wherein modeling the data generating process further comprises: generating a learner object for the dyadic Bayesian model; training the dyadic Bayesian model with the learner object based on the observed data to produce a trained dyadic Bayesian model; generating a posterior distribution over parameters based on the trained dyadic Bayesian model; generating a posterior predictive distribution based on the posterior distribution; and predicting an outcome of observable variables based on the posterior predictive distribution.