We illustrate our VI approach through the use of it to estimate numerous stochastic types. Our most important emphasis is on deep combined products (DMM), that happen to be a class of probabilistic Bayesian neural networks. blended products (also referred to as random coefficient or multi-amount products) are widely accustomed to seize heterogeneit