Networks basic belief

Yet culture is not only deep because it is so fundamental and covert, it is also immensely patterned and therefore related to everything we think, perceive and do. When attempting to change one belief, we have to change many related ones and much that has been built upon these. The large quantities of basic anxiety and the process of mourning mentioned here are quite understandable when such basic belief networks are taken apart. 

ABSTRACT Dependencies or failure dependencies in probabilistic risk assessments may lead to significant errors if not properly analyzed. In order to overcome the limitations of tradition methods, a modified Bayesian Network (BN), which is called Evidential Network (EN), was proposed with evidence theory to handle dependencies in Probabilistic Risk Assessment (PRA). Fault Trees (IT s) and Event Trees (ETs) were transformed into an EN which constructs a uniform framework to represent accident scenarios. Dependencies can be processed through the corresponding evidential networks where uncertainties are characterized by basic belief mass. A case study was discussed to demonstrate the proposed approach. Frequencies of end states were obtained and expressed by belief and plausibility measures. The proposed approach can be easily applied to probabilistic risk assessments that involve dependencies while addresses the uncertainties in experts knowledge. 

In the following, we will first deseribe the related regulatory issues. Then, we will present the basic approach to performing qualitative risk analysis based on Bayesian Belief Network (BBN) to identify unnecessary conservatism. A ease study dealing with the issue of independent verifieation and vahdation (IV V) will then be diseussed, followed by a eonclusion. 

---