Synthesizing Geospatial Data with A Simple NoGAN Technique
If you regularly read my articles, you know that I developed several different techniques for data synthetization. Many are explained in details in my upcoming book Synthetic Data and Generative AI (Elsevier), available here. It includes generative adversarial networks (GANs), copulas, agent-based modeling, methods based on interpolation, correlated noise mixtures, and more. The technique presented […]
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Sound Generation in Python: Turning Your Data into Music
Not long ago, I published here an article entitled “The Sound that Data Makes”. The goal was turning data — random noise in this case — into music. The hope was that by “listening” to your data, you could gain a different kind of insights, not conveyed by visualizations or tabular summaries. This article is […]
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Generated Data vs Monte-Carlo Simulations: What are the Differences?
I sometimes get asked this question: could you use simulations instead of synthetizations? Below is my answer, also focusing on some particular aspects of data synthetizations, that differentiate them from other techniques. Simulations do not simulate joint distributions Sure, if all your features behave like a mixture of multivariate normal distributions, you can use GMMs […]
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