16. One or more computer-readable storage media comprising a plurality of instructions that when executed by a processor, cause the processor to: generate a dyadic Bayesian model comprising a pair of probabilistic functions representing a prior distribution and a sampling distribution from one or more previously-generated dyadic Bayesian models using a model combinator; model a data generating process by transforming data according to the dyadic Bayesian model; generate a learner object for the dyadic Bayesian model; train the dyadic Bayesian model with the learner object based on the observed data 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 based on the posterior predictive distribution.
16. One or more computer-readable storage media comprising a plurality of instructions that when executed by a processor, cause the processor to: generate a dyadic Bayesian model comprising a pair of probabilistic functions representing a prior distribution and a sampling distribution from one or more previously-generated dyadic Bayesian models using a model combinator; model a data generating process by transforming data according to the dyadic Bayesian model; generate a learner object for the dyadic Bayesian model; train the dyadic Bayesian model with the learner object based on the observed data 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 based on the posterior predictive distribution. 20. The one or more computer-readable storage media of claim 16 , comprising using a programming language to generate the dyadic Bayesian model.