{'fraction_non_alphanumeric': 0.031545741324921134, 'fraction_numerical': 0.0, 'mean_word_length': 4.7207207207207205, '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}}
Finite-sample bias in free energy bridge estimators.

In practical free energy estimation, the bias is often neglected once it has been shown to vanish in the large-sample limit. Yet finite-sample bias always exists and ought to be considered in any rigorous study. This work develops a metric for bias in a broad class of free energy "bridge estimators" (e.g., Bennett's method). The framework complements existing variance estimation methods and provides a means for comparing systematic and statistical errors. Examples show that, contrary to what is often assumed, the bias can be quite substantial when the sample size is modest.