Spatial Prediction with Guided Diffusion

Key points

• Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data.1
• The target quantity is the predictive distribution, which is the conditional distribution of the latent spatial process given the observed data.1
• We demonstrate our approach on several simulation studies and in a controlled experiment of methane-flux inversion over the UK and Ireland.1
• We show that guided diffusion can sample from the correct predictive distribution in a variety of settings, that it can be applied to models where MCMC is intractable, and that it scales well with the dimensionality of the latent process.1

Sources (1)

• [1]Spatial Prediction with Guided Diffusion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:09 AM

  Here, we show how recent developments in guided diffusion modelling from machine learning can address a wide class of problems in spatial prediction when the likelihood function derived from the data model is known and differentiable, possibly after relaxation.

  Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data.

Extractive summary: sentences quoted from the sources.