We develop Generative AI (Gen-AI) methods for Bayesian Computation. Gen-AI naturally applies to Bayesian models which are easily simulated. We generate a large training dataset and together with deep neural networks we uncover the inverse Bayes map for inference and prediction. To do this, we require high dimensional regression methods and dimensionality reduction (a.k.a feature selection). The main advantage of Generative AI is its ability to be model-free and the fact that it doesn't rely on densities. Bayesian computation is replaced by pattern recognition of an input-output map. This map is learned from empirical model simulation. We show that Deep Quantile NNs provide a general framework for inference decision making. To illustrate our methodology, we provide three examples: a stylized synthetic example, a traffic flow prediction problem and we analyze the well-known Ebola data-set. Finally, we conclude with directions for future research.

翻译：我们开发了面向贝叶斯计算的生成式人工智能（Gen-AI）方法。对于易于模拟的贝叶斯模型，Gen-AI自然适用。我们生成大规模训练数据集，并结合深度神经网络揭示用于推断和预测的逆贝叶斯映射。为此，需要高维回归方法及降维（即特征选择）。Gen-AI的主要优势在于其无模型特性，且不依赖密度函数。贝叶斯计算被输入-输出映射的模式识别所取代，该映射通过经验模型模拟学习得到。我们证明深度分位数神经网络为推断决策提供了通用框架。为说明方法，我们给出三个示例：一个典型合成示例、一个交通流预测问题，以及对知名埃博拉数据集的剖析。最后，我们提出未来研究方向。