Bayesian inference is a statistical method that updates the probability of a hypothesis as more evidence or information becomes available, utilizing Bayes' Theorem to combine prior beliefs with new data. It provides a flexible framework for modeling uncertainty and making predictions in complex systems, often outperforming traditional methods in scenarios with limited data or evolving conditions.
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.
Conditional GANs (cGANs) extend the traditional Generative Adversarial Networks by incorporating auxiliary information, allowing for more controlled and targeted generation of data. This conditioning can be based on class labels or other data, enabling the generation of specific categories of images or other structured outputs.