Research on uncertainty quantification algorithms based on Bayesian neural networks and a posteriori regularisation

Key Points

• To explore algorithms for uncertainty quantification that utilize Bayesian neural networks and posteriori regularisation techniques.
• Development of Bayesian neural network models
• Implementation of uncertainty quantification algorithms
• Application of posteriori regularisation in the models
• Improved accuracy of predictive models
• Enhanced reliability of uncertainty estimates
• Robustness of algorithms under various scenarios

Abstract