PROJECT-I REPORT: LITERATURE REVIEW
IME864: Risk Analysis
Fall, 2014, Wichita State University, Wichita, KS USA
MODELING OPERATIONAL RISK WITH BAYESIAN NETWORKS AND ITS APPLICATION IN RISK ANALYSIS
Saikiran Mansingh (V767R642)
Affiliation (Industrial Engineering Department, Wichita State University)
The selected respective journal papers discusses and explains the following criteria:
I. In risk analysis, Bayesian methods are more adaptability and flexibility than traditional strategies when be used to construct many decision frameworks, estimate risk distribution and parameterize model, however has shortcomings at the same time. Robust methods and strategies frame some limitations of Bayesian methods, the analysis of uncertainty indicate that robust Bayesian methods can produce and give an output where more reliable inference in the absence of comprehensive statistical information.
II. Due to data limitations and complex as well as complicated interaction between operational risk variables, numerous nonlinear ways have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, its constructs a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real-time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested.
1. INTRODUCTION of Articles
introduction of the background of the identified areas:
(A). Risk analysis refers to a collection of methods address varied problems that arise from...