New Bayesian estimator simplifies probability estimation for large alphabets

Researchers have introduced a novel Bayesian estimator for probability estimation over large alphabets under log loss, a problem previously addressed by methods like the Good-Turing estimator. This new estimator is characterized by its simple construction, involving coordinate-wise multiplication of independent uniform draws from the probability simplex. Its regret, which quantifies the excess code length compared to an ideal code, can be explicitly computed. The estimator demonstrates competitive performance against specialized methods on various benchmarks and reveals scaling laws related to data, alphabet size, and depth. AI

IMPACT Introduces a new statistical method that could improve probability estimation in various AI applications, particularly those dealing with large vocabularies or sparse data.

RANK_REASON The cluster contains an academic paper detailing a new statistical estimator. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Good-Turing estimator
• Hugging Face
• probability simplex
• Zipf targets

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