Early identification of operational failures or faults is of vital importance for the prevention and mitigation of nuclear power plant (NPP) accidents. Various research efforts have been devoted to developing efficient accident diagnostic methods. Among them is the most promising artificial intelligence (AI) technology which makes use of plant status monitoring data for accident identification. However, the AI-based approaches are concerned with the difficulty in combining robustness and interpretability, i.e. the explanations of the features involved in modeling. This paper explores new accident diagnostic methods that are both robust and interpretable based on Bayesian classification algorithms. Specifically, three Bayesian classifiers including discrete naive Bayes classifier, Gaussian naive Bayes classifier, and Bayesian network are implemented and tested with simulation data for a PWR nuclear power plant (NPP) which consists of multiple types of the incident (LOCA, MSLB, SGTB) and dozens of monitoring parameters. The diagnostic accuracy and efficiency of these classifiers are inspected and compared quantificationally for all accident scenarios. The results show the Gaussian Naive Bayes method has the best accuracy which is close to 100%, followed by discrete Naive Bayes (above 97%), while the knowledge-based Bayesian network reveals plain accuracy due to limited usage of simulation data. The Naive Bayes classifiers also outperform Bayesian Network in diagnostic efficiency as they have simpler network structures. The Gaussian Naive Bayes even simplifies the implementation with straightforward data pre-processing.