Systems and methods for generative machine learning

A machine-learning system, comprising:

• at least one processor;
• at least one nontransitory processor-readable medium communicatively coupled to the at least one processor, the at least one nontransitory processor-readable medium which stores at least one of processor-executable instructions or data which, when executed by the at least one processor, cause the at least one processor to attempt to identify the value of at least one parameter that increases the log-likelihood of the at least a subset of a training dataset with respect to a model, and particularly cause the processor to:
• form a latent space comprising a genetic latent subspace and an environmental subspace, each subspace comprising one or more continuous random latent variables;
• form an approximating posterior distribution over the latent space, conditioned on the input space;
• form a prior distribution over the latent space;
• form a decoding distribution over the input space, conditioned on the latent space, the decoding distribution conditioned on the random latent variables of the genetic latent subspace based on genetic covariance induced by familial relationships between organisms;
• and
• train the model based on the encoding, prior, and decoding distributions.