6. Approximators

Disclaimer: This guide is in an early stage. We welcome contributions to the guide in form of issues and pull requests.

Neural approximators provide an approximation of a distribution or a value. To achieve this, they combine the things we have discussed in the previous chapters: simulated data, adapters, summary networks and inference networks. Approximators are at the heart of BayesFlow, as they organize the different components and provide the fit() function used for training.