Generative modeling is a branch of machine learning that focuses on creating models that can generate new data samples that are similar to the training data. These models learn the underlying distribution of the data and use this knowledge to produce new examples that have not been seen before. Generative models are used in a variety of applications such as image and text generation, data augmentation, and anomaly detection. Examples of generative modeling techniques include autoencoders, variational autoencoders, generative adversarial networks (GANs), and flow-based models.