geo-infer-bayes

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.

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.

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.

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.

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.

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.