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geo-infer-bayes

Fit Bayesian models to geospatial data with MCMC, HMC, VI, SMC, and ABC.

13|3|Updated May 13, 2025

Search Tags:#probabilistic modeling #uncertainty quantification #bayesian inference #geospatial analysis #spatial statistics #gaussian processes

Ready to run

npx skills add https://github.com/ActiveInferenceInstitute/GEO-INFER --skill geo-infer-bayes

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: geo-infer-bayes
Source: https://github.com/ActiveInferenceInstitute/GEO-INFER/tree/main/GEO-INFER-BAYES
Command: npx skills add https://github.com/ActiveInferenceInstitute/GEO-INFER --skill geo-infer-bayes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires arviz, cmdstanpy, geopandas, matplotlib, numpy, pandas, pymc, rasterio, scipy, tensorflow-probability, xarray, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to perform advanced statistical analysis on geospatial data, enabling robust inference and decision-making under uncertainty.

Core Features & Use Cases

Probabilistic Modeling: Build sophisticated Bayesian models for spatial and spatio-temporal data.
Uncertainty Quantification: Accurately assess and communicate the uncertainty associated with predictions.
Use Case: Analyze environmental sensor data to predict pollution levels across a region, providing not just the most likely values but also a measure of confidence in those predictions.

Quick Start

Use the geo-infer-bayes skill to fit a spatial Gaussian Process model to the provided data.

Frequently Asked Questions about geo-infer-bayes

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform uncertainty quantification in geospatial analysis?▼

Uncertainty quantification in geospatial analysis is performed by building probabilistic models using Bayesian inference. This Skill fits spatial Gaussian Processes and Hierarchical Bayesian Models to provide not just predictions but confidence measures for spatial data.

Can I use PyMC and Stan for spatial statistics with this framework?▼

Yes, the framework supports spatial statistics using PyMC, Stan, and TensorFlow Probability. It integrates these libraries to execute robust probabilistic modeling and various inference algorithms like MCMC, HMC, VI, SMC, and ABC.

What is the best way to predict environmental pollution levels across a region?▼

Predicting environmental pollution levels across a region is best achieved by fitting a spatial Gaussian Process to sensor data. This approach generates spatial predictions while accurately quantifying the uncertainty associated with each forecasted value.

Does this Bayesian inference framework support spatio-temporal data modeling?▼

The Bayesian inference framework does support spatio-temporal data modeling through sophisticated probabilistic models. Users can build Hierarchical Bayesian Models and leverage libraries like ArviZ and PyMC to analyze spatial and temporal variations.

How do I fit a spatial Gaussian Process to geospatial data?▼

To fit a spatial Gaussian Process to geospatial data, use the provided scripts to configure your model and execute inference algorithms. The framework leverages GeoPandas and rasterio to process spatial inputs and generate probabilistic predictions.

When should I use Approximate Bayesian Computation instead of MCMC for spatial data?▼

Approximate Bayesian Computation is used instead of MCMC for spatial data when dealing with intractable likelihood functions. The framework supports both approaches, allowing users to select the optimal inference algorithm based on model complexity and data structure.