Transformer-based Parameter Estimation in Statistics

arXiv:2403.00019v1 Announce Type: new
Abstract: Parameter estimation is one of the most important tasks in statistics, and is key to helping people understand the distribution behind a sample of observations. Traditionally parameter estimation is done either by closed-form solutions (e.g., maximum likelihood estimation for Gaussian distribution), or by iterative numerical methods such as Newton-Raphson method when closed-form solution does not exist (e.g., for Beta distribution).
In this paper we propose a transformer-based approach to parameter estimation. Compared with existing solutions, …

abstract arxiv cs.lg distribution form iterative key likelihood maximum likelihood estimation numerical people sample solutions statistics stat.ml tasks transformer type