Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Efficient methods for Gaussian Markov random fields under sparse linear constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the constraint-basis method instead of the standard GMRF method for likelihood evaluation | GMRF with sparse hard constraints · Matérn field point observations | computation time: more than 1000 observations | Helped |
| Use the constraint-basis method instead of conditioning by kriging for sampling | GMRF with sparse hard constraints · Matérn field point observations | computation time | Helped |
| Use covariance-based inference instead of the constraint-basis GMRF method | Matérn field with point observations · Matérn field point observations | computation time: approximately 1000 observations | Mixed |
| Use the SPDE-based constrained GP model instead of the covariance-based GP model | Constrained Gaussian process regression · Divergence-free bivariate function reconstruction | RMSE | No clear effect |
| Use the SPDE-based method instead of the covariance-based method for prediction | Constrained Gaussian process regression · Divergence-free bivariate function prediction | computation time: m>600 | Helped |
| Use the covariance-based method instead of the SPDE-based method for prediction | Constrained Gaussian process regression · Divergence-free bivariate function prediction | computation time: m>600 | Mixed |
Closest papers by meaning
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- Incremental Variational Sparse Gaussian Process Regression (NeurIPS 2016 · similarity 0.86)
- SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data (ICML 2021 · similarity 0.86)
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