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arXiv Machine Learning

Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model

The paper introduces a lightweight algorithm for modeling tabular data with both continuous and discrete variables. It transforms data into a continuous latent space using empirical marginals and a quantile function, enabling efficient estimation with distributions like a multivariate Gaussian. The method preserves complex marginal distributions and correlations, and handles discrete variables via a Monte Carlo expectation‑maximisation framework that allows conventional maximum‑likelihood estimation.

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