PyMC Data Handling

What problem does it solve?

PyMC users often struggle to manage changing datasets within a single model, needing reliable data containers that can be updated between fits and predictions.

Core Features & Use Cases

• Data container creation and updates with pm.Data to reflect new inputs during training and forecasting.
• Mini-batch support with pm.Minibatch for scalable stochastic optimization on larger datasets.
• Real-world workflow examples include train/test splits, cross-validation, and updating coordinates for predictions.

Quick Start

Provide a minimal PyMC model that uses Data and Minibatch to demonstrate updating data containers and running a small posterior sample.