Explore practical steps for generating synthetic data using Bayesian sampling and univariate distributions, crucial for domains like healthcare and finance where real data can be scarce or sensitive. The guide discusses both probabilistic and generative approaches for creating realistic datasets, leveraging Python libraries like bnlearn and distfit for modeling and distribution fitting. It underscores the importance of validating synthetic data to avoid biases and misrepresentations.
Table of contents
An Introduction To Synthetic DataWhat You Need To Know About Probability Density FunctionsWhat You Need To Know About Bayesian SamplingThe Predictive Maintenance DatasetGenerate Continuous Synthetic DataGenerate Categorical Synthetic DataThe bnlearn libraryWrapping upSort: