BAYESIAN MODELING FOR RISK ASSESSMENT BASED ON INTEGRATING INFORMATION FROM DYNAMIC DATA SOURCES

Abstract

Bayesian modeling can be used for risk assessment. For example, a computing device determines, using a Bayesian prediction model, a risk indicator for a target entity from predictor variables associated with the target entity. The Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model. The Bayesian prediction model is generated based on an initial training dataset. The initial training dataset includes training records and predictor variables. The Bayesian prediction model can be generated by calculating the set of parameters based on the initial training dataset. The Bayesian prediction model can be updated by updating the set of parameters using an additional training dataset. The computing device transmits, to a remote computing device, the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

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