The Bayesian approach to data analysis provides a consistent and flexible way to handle uncertainty in all observations, model parameters, and model structure using probability theory. However, building Bayesian models in a principled way remains a highly complex task requiring a lot of expertise and cognitive resources. In this project, we will develop a machine assisted workflow for building interpretable, robust, and well-predicting Bayesian models. Based on statistical theory, we will develop a framework for simulating realistic data with known modeling challenges using generative adversarial networks. Subsequently, using neural network architectures tuned to the structure of the fitted Bayesian models, machines will be trained on the simulated data to provide automatic model evaluation and modeling recommendations that guide the user through the model building process using interactive visualizations. While leaving the modeling choices up to the user, the machine subsequently learns from the user's decisions to improve its recommendations on the fly. The developed approaches will be applied together with collaborators both within and outside of SimTech to help them improve their Bayesian data analysis workflow.