{'fraction_non_alphanumeric': 0.014634146341463415, 'fraction_numerical': 0.0, 'mean_word_length': 5.785123966942149, '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}}
Graphical representation of IPA estimation

Infinitesimal Perturbation Analysis (IPA) estimators of the response gradient for a discrete event stochastic simulation are typically developed within the framework of Generalized semi-Markov processes (GSMPs). Unfortunately, while mathematically rigorous, GSMPs are not particularly useful for modeling real systems. We describe a procedure that allows IPA gradient estimation to be easily and automatically implemented in the more general and intuitive modeling context of event graphs. The intent is to make IPA gradient estimation more easily understood and more widely accessible. The pictorial nature of Event Graphs also provides insights into the basic IPA calculations and alternative descriptions of conditions under which the IPA estimator is known to be unbiased.