We illustrate our VI technique by making use of it to estimate various stochastic styles. Our major concentration is on deep combined styles (DMM), which might be a category of probabilistic Bayesian neural networks. blended types (also known as random coefficient or multi-degree styles) are widely used to capture heterogeneity in statistical model