Pages that link to "Item:Q5226629"
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The following pages link to Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping (Q5226629):
Displaying 11 items.
- Parallel cross-validation: a scalable fitting method for Gaussian process models (Q829752) (← links)
- Bayesian multi-resolution modeling for spatially replicated data sets with application to forest biomass data (Q997307) (← links)
- A case study competition among methods for analyzing large spatial data (Q2272997) (← links)
- Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains (Q5885120) (← links)
- Constrained Functional Regression of National Forest Inventory Data Over Time Using Remote Sensing Observations (Q6044624) (← links)
- Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data (Q6079471) (← links)
- Structured prior distributions for the covariance matrix in latent factor models (Q6581682) (← links)
- Nearest-neighbor sparse Cholesky matrices in spatial statistics (Q6602373) (← links)
- High-dimensional multivariate geostatistics: a Bayesian matrix-normal approach (Q6626391) (← links)
- Analyzing environmental-trait interactions in ecological communities with fourth-corner latent variable models (Q6626404) (← links)
- Conjugate sparse plus low rank models for efficient Bayesian interpolation of large spatial data (Q6626522) (← links)
