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  1. Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo

    Publication
    Hamiltonian Monte Carlo (HMC) is the mainstay of applied Bayesian inference for differentiable models. However, HMC still struggles to sample from hierarchical models that induce densities with multiscale …

  2. Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions

    Publication
    The efficiency of Hamiltonian Monte Carlo (HMC) can suffer when sampling a distribution with a wide range of length scales, because the small step sizes needed for stability …

  3. Galaxy clustering analysis with SimBIG and the wavelet scattering transform

    Publication
    The non-Gaussian spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameters. We conduct Bayesian …

  4. Variational Inference with Gaussian Score Matching

    Publication
    Variational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics. Typically, VI fits a simple parametric distribution to be close …

  5. pmwd: A Differentiable Cosmological Particle-Mesh N-body Library

    Publication
    The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentiable simulations provide gradients of …

  6. Differentiable Cosmological Simulation with Adjoint Method

    Publication
    Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and …