Evolving approaches with probabilistic programming and computational statistics are promising powerful methods to define and create analytical systems. These methods can represent probabilistic models under a variety of conditions and constraints, and are capable of simulating new data from these states by the generative nature of these models. HeartAI deployments supports inference from a Bayesian perspective, such as by generating samples from a prior or posterior predictive distribution, where there may also be a conditioning or marginalisation of such a distribution. In addition to the creation of a representative probabilistic construct, these models may be used for the generation of new data under a variety of assumptions and hypothetical situations, and allow for the prediction and forecasting of future events and potential outcomes.

Further information about HeartAI analytics may be found with the following documentation: