Reproducible MCMC Pipeline for Hubble Constant (H₀) Tension Analysis — Minimal Demonstration Package

This repository provides a minimal, self-contained package demonstrating a reproducible Markov Chain Monte Carlo (MCMC) workflow for Hubble constant (H₀) tension studies in cosmology. It accompanies the Jeffrey 21×21 Reproducibility Dataset and illustrates how summary measurements, correlation structures, and systematic bias terms can be propagated through a Bayesian inference pipeline. DOINfor that minimal dataset is https://doi.org/10.5281/zenodo.17445244 The package includes: measurements.csv — compact input dataset of H₀ constraints, correlation_matrix_21x21.csv — corresponding correlation matrix, systematic_bias.json — toy example of bias priors, run_mcmc.py — reproducible inference driver script, example_notebook.ipynb — demonstration of posterior recovery, and a requirements.txt for full environment reproducibility. This minimal release serves as a transparent, testable example of the Bayesian backbone for the forthcoming comprehensive Hubble Tension Resolution Analysis. It aims to help reviewers, educators, and researchers reproduce baseline MCMC behavior prior to the full-scale public dataset release.