Synthetic data generation

Synthetic data generation is the process of using generative algorithms to create artificial, AI-generated datapoints that are statistically and structurally identical to their real-world counterparts. These generative models use data samples as training data and learn the correlations, statistical properties, and data structures of the samples. There are several different approaches for creating synthetic data. These range from basic techniques that simply draw numbers to more sophisticated methods that rely on statistical machine learning models. Generative modeling is one of the most advanced techniques for generating synthetic data. These models are able to automatically discover the underlying model in the data and use that model to produce new datapoints that closely match the distribution of the real-world data they were trained on. This approach is useful for a variety of reasons, including allowing analysts to use the data without having to know exactly what the under...