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arXiv Machine Learning By Maria Nareklishvili, Nick Polson, Vadim Sokolov

Generative Modeling: A Review

Read the original on arXiv Machine Learning →

The paper reviews generative modeling by categorizing generators into three types: those estimating counterfactual outcome distributions in causal inference, those recovering posteriors from simulated parameter–outcome pairs, and those forming predictive outcome distributions. It introduces generative Bayesian computation, a quantile neural network trained on simulated pairs using the pinball loss, which directly targets posterior distributions without requiring invertible architectures or density evaluation. The method is demonstrated on an agent-based Ebola transmission model, showing accurate posterior recovery at lower computational cost than rejection-based simulation inference.

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