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

**[Towards Data Science](https://daily.dev/sources/tds)** · 34 min read · 0 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/how-to-generate-synthetic-data-a-comprehensive-guide-using-bayesian-sampling-and-univariate-distributions/>

---

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