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.
Cambridge University Press
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
HEDGE generates hypergraphs via a linear-Gaussian forward diffusion on incidence matrices with a hypergraph-specific heat operator, then learns a permutation-equivariant reverse drift to sample from the Gaussian base.
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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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Hypergraph Generation via Structured Stochastic Diffusion
HEDGE generates hypergraphs via a linear-Gaussian forward diffusion on incidence matrices with a hypergraph-specific heat operator, then learns a permutation-equivariant reverse drift to sample from the Gaussian base.