Year: 2025
Type: article
Abstract: Bayesian methods are prominent in data processing. Based on Bayes' theorem, they dynamically update knowledge and probabilistic judgments about events using prior information and observed data, enabling more precise analysis and prediction. In terms of application, Bayesian methods are widely used in numerous fields such as machine learning, medical diagnosis, and financial risk assessment. This paper focuses on their application and development in this field. Using literature analysis, it explores two research questions: the current application status and future development directions. The study reveals that Bayesian methods are mainly used in parameter optimization and risk modeling/decision - making. They offer advantages like efficient parameter tuning and flexible model selection. However, challenges such as hyperparameter sensitivity exist. Future research could integrate more cross - domain knowledge and develop a probabilistic knowledge base for heritage conservation.
Source: Theoretical and Natural Science
Author: Shichen Zhang
Institution: Dalian University of Technology
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414145583