Bayesian inference, as a probabilistic reasoning framework grounded in Bayes’ theorem, demonstrates remarkable flexibility and adaptability in the field of modern statistics. However, traditional methods often encounter computing bottlenecks and model constraints when dealing with complex models and massive datasets. To facilitate the integration of theoretical development and practical applications, this paper systematically explores the diverse applications of Bayesian inference in big data analysis, machine learning, and medical research, and delves into the cutting-edge developments in efficient computation, complex model construction, and multidisciplinary integration. A comprehensive understanding and optimization of Bayesian inference hold significant practical value for enhancing modern statistical analysis capabilities and addressing complex problems.

Bayesian inference;Modern statistics;Application;Development

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DOI： https://doi.org/10.62639/sspjinss03.20250201

Published： 2025