B-GRASP: A Bayesian Framework for Inferring Graph Weights from SPDE-Inspired Dynamics

arXiv:2609.39519 · stat.ME, physics.data-an, q-bio.PE, stat.CO · Submitted 2026-09-30 · Read on arXiv

stat.ME, physics.data-an, q-bio.PE, stat.CO

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/babakmaboudi/B-GRASP

Terminology

• Bayesian Framework — 24× in this paper · explained in 3 episodes
• Inverse Problem — 18× in this paper · explained in 2 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Uncertainty Quantification — 8× in this paper · explained in 8 episodes
• Adjacency Matrix — 5× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Hamiltonian Monte Carlo — 2× in this paper
• Heterogeneous Graph — 2× in this paper · explained in 2 episodes
• No-U-Turn Sampler — 2× in this paper
• Stochastic Dynamics — 2× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• The SPDE Approach to Mat'ern Fields: Graph Representations

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