| 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
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 actual Cholesky decomposition
- 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
