geo-infer-bayes
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.
Best use case
geo-infer-bayes is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using geo-infer-bayes should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-BAYES/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-bayes Compares
| Feature / Agent | geo-infer-bayes | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-BAYES ## 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 ```python 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 ```python 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