Generative Modeling: A Review
This is an incremental review paper summarizing existing generative AI techniques for researchers in machine learning and statistics.
The paper reviews generative modeling methods, focusing on their application to machine learning and Bayesian inference tasks, and illustrates these methods using the Ebola dataset.
Generative methods (Gen-AI) are reviewed with a particular goal of solving tasks in machine learning and Bayesian inference. Generative models require one to simulate a large training dataset and to use deep neural networks to solve a supervised learning problem. To do this, we require high-dimensional regression methods and tools for dimensionality reduction (a.k.a. feature selection). The main advantage of Gen-AI methods is their ability to be model-free and to use deep neural networks to estimate conditional densities or posterior quintiles of interest. To illustrate generative methods , we analyze the well-known Ebola data set. Finally, we conclude with directions for future research.