Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation-Based Inference

Data and results used in the paper "Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation-Based Inference" by Wolniewicz et al. The code used to work with this data can be found on GitHub: https://github.com/hawaii-ai/GalacticCosmicRays. d1_b1_init1_changing_hmc_init_sample_files.zip: all posterior samples for changing HMC initial states (used in part to create Figures 4, 5, 6, 7, 12, and 13). Naming convention of sample and model files: There are two kinds of dataset sampling: b1 for bootstrap-sampled training data, and b0 for non-bootstrapped. b0 is only used for training on the entire training set. There are 5 random HMC re-initializations: hmc1, hmc2, hmc3, hmc4, and hmc5 There are 5 bootstrap sampled datasets: d1, d2, d3, d4, and d5 There are 5 NN model re-initializations: init1, init2, init3, init4, and init5 Training data is of size: 0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 Dataset 2/4 contains the AMS-02 and PAMELA posterior samples for changing HMC initial states, which are needed to recreate Figures 4, 5, 6, 7, 12, and 13 from the paper.