MGD: Moment Guided Diffusion for Maximum Entropy Generation
Read the original on arXiv Machine Learning →The paper introduces Moment Guided Diffusion (MGD), a new method that blends diffusion-based generative modeling with classical maximum entropy techniques. MGD samples maximum entropy distributions by solving a stochastic differential equation that steers moments toward specified values in finite time, thereby avoiding the slow mixing of traditional MCMC or Langevin dynamics. The authors prove convergence to the maximum entropy distribution in the large-volatility limit and provide a tractable entropy estimator, demonstrating the method on financial time series, turbulent flows, and cosmological fields using wavelet scattering moments.
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