Posterior samples of the parametric MCMC and of the B-PINN Hamiltonian Monte Carlo

This dataset contains the posterior samples generated via parametric Markov Chain Monte Carlo (MCMC) and Bayesian Physics-Informed Neural Networks (B-PINN) using Hamiltonian Monte Carlo (HMC). These samples are part of the reproducibility artifacts for the paper: "A data-centric approach to identifying the damping law of a U-tube oscillator with Bayesian model comparison and Bayesian physics-informed neural networks". The data corresponds to the analysis of eight liquid oscillation recordings in a U-tube (U1–U8). The raw experimental data is openly available at: https://github.com/teaching-physics/liquid-oscillations-Utube (García-Farieta and Orjuela, 2022). The analysis code used to generate, process, and plot these posteriors is available at: https://github.com/jsuarez314/bayesian-ml-physics-lab