A BBN is a graphical network that represents probabilistic relationships among events in a network structure.

With BBNs, it is possible to articulate expert beliefs about the dependencies between different variables and to propagate consistently the impact of evidence on the probabilities of uncertain outcomes, such as 'future system reliability' [Falla, Ch4]

The BBN on the left uses comparatively little evidence, depending only on the observed reliabilities and defect counts of previous products of the same process, and on the defects discovered in the current product during debugging. The topology of the graph is used to indicate probabilistic relationships among the variables described in the nodes. The BBN on the right includes subjective indicators, like problem complexity and design effort. Thus, this network is meant to be populated with probabilities that are not all derived from statistical inference, but at least in part from expert opinion.

BBNs are also sometimes called Causal Probabilistic Networks, Probabilistic Cause-Effect Models or Probabilistic Influence Diagrams

See Agena
See Modist

The Bellcore approach is widely used in the telecommunications industry and has been updated to SR-332 (in May 2001). Bellcore's approach is very similar to that of MIL-HDBK-217 but it's based primarily on telecommunications data and covers five separate use environments. The approach also assumes an exponential failure distribution and calculates reliability in terms of failures per billion part operating hours, or FITs. Its empirically based models are in three categories: the Method I parts count approach that applies when there is no field failure data available, the Method II modification to Method I to include lab test data and the Method III variation that includes field failure tracking.

• Method I includes a first year modifier to account for infant mortality.
• Method II includes a Bayes weighting procedure that covers three approaches depending on the level of previous burn-in the part or unit has undergone.
• Method III includes a Bayes weighting procedure as well but it is based on three different cases depending on how similar the equipment is to that from which the data was collected.

For the most widely used Method I case where the burn-in varies, the steady-state failure rate depends on the basic part steady-state failure rate and the quality, electrical stress and temperature factors as follows: