{'fraction_non_alphanumeric': 0.03202328966521106, 'fraction_numerical': 0.017467248908296942, 'mean_word_length': 4.880341880341881, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 1, '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}}
A stochastic multisample extension of Morris's robust detector in bounded amplitude noise

A Bayesian approach to the problem of finite sample detection of a signal in an unknown noise environment is formulated. A mathematical solution is given for the minimax (robust) test for detecting the presence of a stochastic signal of known prior probability in unknown additive noise of bounded magnitude. This solution remains valid for a constant signal with identical components when the additive noises are independent. This extends results of J.M. Morris (ibid., vol.IT-62, p.199-209, March 1980). Worst-case performance bounds for detecting the presence of a pattern class are derived. >