{'fraction_non_alphanumeric': 0.015116279069767442, 'fraction_numerical': 0.0, 'mean_word_length': 5.194244604316546, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Uncertainty of Monte Carlo variance estimates: application to the simulation of x-ray imaging detectors

Knowledge of the uncertainty associated with Monte Carlo estimates is useful for determining when to stop a simulation run when statistical uctuations fall below a desired tolerance level, and for designing and analyzing variance reduction techniques. In this work, we discuss how to analytically calculate the uncertainty of Monte Carlo variance estimates from higher order moments of the distribution of events. In addition, we show how these expressions can be incorporated in the study of x-ray imaging detectors to manage runtime and precision of the simulation. Our analysis can be used to design variance reduction techniques for Monte Carlo simulations when, as in many cases in imaging, the variance and not the mean, is the quantity of interest.