Even though the use of Bayesian Networks is widely used in solving matters that are uncertain, the application of the Bayesian Networks may not accurate and sufficient in terms of solving different kinds of real-world problems. In line with this, RADAR recognizes the fact that scaling up the use of the Bayesian Networks model can only discourage a lot of non-experts from using this methods (RADAR, 2010). Likewise, many people believe that it is not practical to create huge models in solving real-world problems (ibid).
The purpose of the research study is to examine, compare and contrast the existing peer-reviewed studies on scaling-up of Bayesian Networks. Based on the research findings, recommended ways on how to address the two fundamental concerns of RADAR will be provided.
RADAR recognizes the fact that scaling up the use of the Bayesian Networks model can only discourage a lot of experts and non-experts from using the model. On top of this, many people believe that it is not practical to create complicated models in solving real-world problems.
Since RADAR has already publicized their major concerns with regards to the scaling-up of Bayesian Networks, there is a strong need to determine some ways on how we can simplify the use of the Bayesian Networks while maintaining its efficiency in solving real-world problems. This is the only way we can strengthen the trust of large-scale companies with regards to the use of Bayesian Network technique.
Considering the two fundamental concerns of RADAR with regards to scaling-up the Bayesian Networks, a literature review will be conducted to examine, compare and contrast some of the previous studies that has been conducted concerning the scaling-up of Bayesian Networks. On top of the literature review, the researcher will conduct a one-on-one interview with a couple of professionals who are currently working for RADAR Group.
Aside from the use of online search engines, the researcher will