Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

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.

How to Generate Synthetic Data: A Comprehensive Guide Using Bayesian Sampling and Univariate Distributions

What You Need To Know About Probability Density Functions

What You Need To Know About Bayesian Sampling