Tutorial Overview

Step-by-step guides to common vplanet_inference workflows. Each tutorial is available as a downloadable Jupyter notebook.

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#	Notebook	Description
1	Setting Up and Running VPLanet Models ↓	Set up VPLanet input files, run a stellar evolution model from the command line, understand the output files, and run the same model through vplanet_inference with unit-aware input/output parameters.
2	Global Sensitivity Analysis with vplanet_inference ↓	Run a Sobol variance-based global sensitivity analysis on a binary star tidal evolution model to identify which input parameters drive the most variance in the outputs. Covers the SALib workflow, parallel model evaluation, and heatmap visualization, as well as the YAML-driven AnalyzeVplanetModel convenience class.
3	MCMC with VPLanet and alabi ↓	Infer TRAPPIST-1 stellar parameters from observed luminosities using a custom likelihood function. Covers direct MCMC with dynesty and emcee, the alabi surrogate model approach for expensive forward models, and advanced likelihood patterns (Student-t, multi-observable, failed-run handling).