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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A tutorial that unifies diffusion probabilistic models, score-based generative modeling, and SDE methods by deriving forward and reverse dynamics from a shared Gaussian noising process.
citing papers explorer
-
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.
-
A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models
A tutorial that unifies diffusion probabilistic models, score-based generative modeling, and SDE methods by deriving forward and reverse dynamics from a shared Gaussian noising process.