A method for generating a computer-implemented machine learning system. The method includes receiving a training data set, which corresponds to a dynamic response of a device, and computing an aggregation of at least one latent variable of the machine learning system, using Bayesian inference, and in view of the training data set. An information item contained in the training data set is transferred directly into a statistical description of the plurality of latent variables. The method further includes generating an a-posteriori predictive distribution for predicting the dynamic response of the device, using the calculated aggregation, and under the condition that the training data set has set in.
BAYESIAN CONTEXT AGGREGATION FOR NEURAL PROCESSES