**4. Conclusions**

Going back to the beginning of the chapter, "What to do when you have a complex question with numerous variables that are not well understood?" It would appear that the use of hierarchical predictive Bayesian models is a solution to address the challenge. While there may be circumstances where these methods may not work (psychology), for issues such as infrastructure and completely unknown questions such as the Drake equation, the methods seem ideally situated to shed light on the solution in a probabilistic form. The outcome of these methods provides a probability of a given answer—not a specific answer—at different levels of confidence. Uncertainty and variability are by the nature of being a probabilistic answer already included in the solutions. This is why added data can improve the likelihood of a given solution while reducing the potential for less likely solutions. Like the Drake equation solution, the dose–response example is an example of such a process. Infrastructure would be as well—as more data are created, the solutions become more robust and uncertainty is reduced. The results permit us to make better decisions as the data improve our understanding.

**Figure 8.** *Partial diagram of potential hierarchical predictive Bayesian infrastructure risk model.*

*Applications of Hierarchical Bayesian Methods to Answer Multilayer Questions with Limited… DOI: http://dx.doi.org/10.5772/intechopen.104784*
