The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Model Validation
Abstract
Though computational models take a lot of effort to build, a model is generally not useful unless it can help people to understand the world being modelled, or the problem the model is intended to solve. A useful model allows people to make useful predictions about how the world will behave now and possibly tomorrow. Validation is the last step required in developing a useful Bayesian model. The goal of validation is to gain confidence in a model and to demonstrate and prove that a model produces reliable results that are closely related to the problems or issues in which the model is intended to address. The goal of the Chapter is to provide the reader with a basic understanding of the validation process and to share with them key lessons learned from the model of social capital presented in the book. While sensitivity analysis is intended to ensure that a Bayesian model is theoretically consistent with goals and assumptions of the modeller (how the modeller views the world) or the accuracy of sources of data used for building the model, the goal of validation is to demonstrate the practical application of the model in real world settings. This Chapter presents the main steps involved in the process of validating a Bayesian model. It illustrates this process by using examples drawn from the Bayesian model of social capital.
Related Content
|
K. Muthamil Sudar.
© 2027.
26 pages.
|
|
Indranil Saha, Anuva Aggarwal, Taher Aurangabadi, Zeesha Mishra.
© 2027.
36 pages.
|
|
Qais Al-Na'amneh.
© 2027.
24 pages.
|
|
Zeesha Mishra, Dhruvika Bansal, Garvit Bajaj.
© 2027.
42 pages.
|
|
Amrutha Kolhar, Sridevi.
© 2027.
32 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Jaime Aguilar-Ortiz, Francisco R. Trejo-Macotela, Eric Simancas-Acevedo.
© 2027.
38 pages.
|
|
Semila Fernandes, Anshul Dhunna.
© 2027.
40 pages.
|
|
|