Four major steps are entailed in generating successful probabilistic modelling through the Bean Machine. The modelling is based on generative techniques, the data collected from Python dictionaries where it is associated with random variables. The learning step improves the model’s knowledge based on observations, and the results are stored for further analysis.
Through probabilistic modelling, engineers and data scientists can account for random events in future predictions while measuring different uncertainties. This method is preferred because it offers uncertainty estimation, expressivity, and interpretability facilities. Let’s discuss these.