MEGPODE decomposes subject-specific ODE vector fields into population and individual Gaussian process priors and uses Kalman smoothing with virtual collocation to enable efficient Bayesian mixed-effects inference for heterogeneous dynamical systems.
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A forward-only Lanczos gradient approximation for Hermitian matrix function bilinear forms whose error scales with the same residual norm as the forward approximation and appears stable without reorthogonalization.
S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.
citing papers explorer
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Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
MEGPODE decomposes subject-specific ODE vector fields into population and individual Gaussian process priors and uses Kalman smoothing with virtual collocation to enable efficient Bayesian mixed-effects inference for heterogeneous dynamical systems.
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Fast and Stable Gradient Approximation for Bilinear Forms of Hermitian Matrix Functions
A forward-only Lanczos gradient approximation for Hermitian matrix function bilinear forms whose error scales with the same residual norm as the forward approximation and appears stable without reorthogonalization.
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Stochastic Smoothed Particle Hydrodynamics for Stochastic Mechanics Problems
S-SPH extends mesh-free SPH to stochastic problems via polynomial chaos and KL expansions, delivering mean and variance statistics that match Monte Carlo at up to 1000 times lower cost on benchmark advection and Burgers flows.
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Laplace Variational Inference for Dirichlet Process Mixtures of Marked Poisson Point Processes
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
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Neural Stochastic Processes for Satellite Precipitation Refinement
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.