Bayesian Regularization

Bayesian regularization is a technique used to improve the generalization and robustness of machine learning models by incorporating prior knowledge or assumptions about the model parameters. Current research focuses on applying Bayesian regularization to diverse models, including neural networks, random forests, and Markov decision processes, often within the context of variational inference or Markov Chain Monte Carlo methods for efficient computation. This approach addresses overfitting and improves uncertainty quantification, leading to more reliable predictions in various applications such as tool wear prediction, medical image analysis, and system identification, ultimately enhancing the accuracy and trustworthiness of machine learning models across scientific disciplines and practical domains.