Automated summary

Performs Bayesian inference and probabilistic modeling for geospatial data, including MCMC, Gaussian processes, and hierarchical models.

Strengths

Implements full MCMC and variational inference methods
Supports model comparison via LOO, WAIC, DIC
Uses Cholesky decomposition for Gaussian processes
Integrates with PyMC and TensorFlow Probability

Limitations

Requires advanced probabilistic knowledge
May be computationally intensive for large datasets
Gaussian processes limited to moderate dimensions

When to use it

Best for estimating uncertainty and parameters in complex spatial models with georeferenced data.

When not to use it

Avoid for simple analyses or non-spatial data, or when computation time is severely constrained.

Security analysis

Safe

Quality score88/100

The skill describes a Bayesian inference library for geospatial data. It includes only import statements, a test command (pytest on a local directory), and no destructive or exfiltration operations. There is no use of curl, shell commands, or disabling of safety measures. The risk is minimal.

No concerns found

How this analysis is made

Examples

Fit a Bayesian hierarchical model

Using geo-infer-bayes, fit a Bayesian hierarchical model with partial pooling to the geospatial dataset 'land_use.csv' with priors, run MCMC for 2000 samples, and compute WAIC.

Compare two geospatial models

Compare a Gaussian process model and a linear Bayesian model on the spatial data using LOO-CV with geo-infer-bayes.

name: geo-infer-bayes description: Bayesian inference and probabilistic modeling for geospatial data. Use when building hierarchical models, computing posteriors with PyMC or TFP, performing variational inference, model comparison (LOO/WAIC/DIC), or spatial Gaussian processes. prerequisites: required: - geo-infer-math recommended: - geo-infer-space - geo-infer-data difficulty: advanced estimated_time: 60min examples_dir: ../GEO-INFER-EXAMPLES/examples/

GEO-INFER-BAYES

Name: Bayesian Inference for Geospatial Data
Author: Skills Guide Bot

Instructions

Core Capabilities

Bayesian inference: Full posterior computation via MCMC and variational methods
Model comparison: LOO-CV, WAIC, DIC, BIC, AIC (all real implementations)
Gaussian processes: Cholesky-decomposition GP with multiple kernels
Hierarchical models: Partial pooling via Cholesky LKJ decomposition
Prior specification: Jeffreys, reference, unit-information priors
ELBO computation: Real evidence lower bound (not placeholder)

Key Imports

from geo_infer_bayes.core.bayesian_inference import BayesianModel
from geo_infer_bayes.core.gaussian_process import GaussianProcess
from geo_infer_bayes.core.variational import VariationalInference
from geo_infer_bayes.api.pymc_interface import PyMCInterface
from geo_infer_bayes.api.tfp_interface import TFPInterface

Examples

from geo_infer_bayes.core.bayesian_inference import BayesianModel

model = BayesianModel(prior="normal", likelihood="normal")
posterior = model.fit(data, n_samples=2000)
comparison = model.compare(["model_a", "model_b"], method="loo")

Guidelines

GP uses Cholesky decomposition (real, not stub)
TFP interface: real GP + Metropolis-Hastings sampling
PyMC interface: posterior predictive sampling for predictions
Variational: real ELBO computation with KL divergence
Test: uv run python -m pytest GEO-INFER-BAYES/tests/ -v

Integrations

ACT → Active Inference belief updating and free energy
MATH → Spatial statistics feeding Bayesian models
SPM → Bayesian GLM fitting for parametric maps
AI → Bayesian hyperparameter optimization
RISK → Bayesian uncertainty quantification for risk

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mkdir -p '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/bayesian-inference-for-geospatial-data-mm78nojn/download' --output '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn/SKILL.md.part' && mv '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn/SKILL.md.part' '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn/SKILL.md'

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Claude Code

test -f '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn/SKILL.md' && test -s '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/geo-infer-bayes' && cp -R '.skill-downloads/bayesian-inference-for-geospatial-data-mm78nojn' '.claude/skills/geo-infer-bayes'

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Skills Guide Bot