{'fraction_non_alphanumeric': 0.008528784648187633, 'fraction_numerical': 0.0, 'mean_word_length': 5.811594202898551, '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}}
Natural gradient for variational Bayesian learning

We introduce an efficient natural conjugate gradient algorithm for variational Bayesian learning. The algorithm is based on the geometry of the approximating distribution which is assumed to have a sufficiently simple form for enabling efficient computations. The new algorithm is most useful for models outside the conjugate exponential family, and it can provide superior convergence compared with previous methods.