Anisotropic Gaussian Fields Parameterization with Penalized Complexity Priors

A 2026 study by L. Llamazares-Elias introduces a smooth parameterization for anisotropic Gaussian random fields. The work constructs penalized complexity priors for the model's correlation length and diffusion matrix parameters. The dataset includes a PDF and TXT file totaling 5.6 MB, published under a CC-BY-4.0 license.

Use Cases

• Specifying penalized complexity priors based on the described correlation length and diffusion matrix parameterization.
• Implementing Bayesian inference for anisotropic Gaussian fields based on the stochastic partial differential equation framework.
• Studying the effect of weakly informative priors on posterior covariance structures in spatial models.

Strengths

• Includes a 5.6 MB PDF document detailing the methodological framework.
• Provides a specific parameterization for correlation length and diffusion matrix of anisotropic Gaussian fields.
• Formulates penalized complexity priors designed to push the correlation range toward infinity and anisotropy to zero.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Theoretical/methodological research paper.

Freshness

Last updated 2026-05-18 13:52:03; freshness should be verified.