Unlocking Fault Diagnosis Secrets: How Prior Information Enhances Inference Accuracy
The article discusses how using prior information in Bayesian inference can help improve fault diagnosis. By combining training data with response and causality information, constraints on the posterior distribution can be established. These constraints are expressed through the prior probability distribution, allowing for more accurate computations. Overall, incorporating prior information enhances the inference process.
