Add to ORKGThe effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by subjecting the network to a sensitivity anal-ysis. Having detailed the sensitivity functions involved in our previous work, we now study the effect of parameter inaccuracies on a recom-mended decision in view of a threshold decision-making model. We describe the effect of varying one or more parameters from a conditional prob-ability table and present a computational pro-cedure for establishing bounds between which assessments for these parameters can be varied without inducing a change in the recommended decision. We illustrate the various concepts by means of a real-life dynamic network in the field of infectious disease.
A probabilistic network built for an application domain often has a single output variable of intere...
Several recent works show that sensitivity analysis (SA) of decision-support models shares distincti...
Several recent works show that sensitivity analysis (SA) of decision-support models shares distincti...
Sensitivity analysis is a general technique for investigating the robustness of the output of a math...
Abstract. Sensitivity analysis is a general technique for investigating the robust-ness of the outpu...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
The process of building a Bayesian network model is often a bottleneck in applying the Bayesian netw...
When building a Bayesian belief network, usually a large number of probabilities have to be assessed...
The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic...
AbstractEmpirical evidence shows that naive Bayesian classifiers perform quite well compared to more...
The sensitivities revealed by a sensitivity anal-ysis of a probabilistic network typically depend on...
The paper discusses the problem of sensitivity analysis in normal Bayesian networks. The algebraic s...
When using a computer model to inform a decision, it is important to investigate any uncertainty in ...
The assessments for the various conditional probabilities of a Bayesian belief network inevitably ar...
A probabilistic network built for an application domain often has a single output variable of intere...
A probabilistic network built for an application domain often has a single output variable of intere...
Several recent works show that sensitivity analysis (SA) of decision-support models shares distincti...
Several recent works show that sensitivity analysis (SA) of decision-support models shares distincti...
Sensitivity analysis is a general technique for investigating the robustness of the output of a math...
Abstract. Sensitivity analysis is a general technique for investigating the robust-ness of the outpu...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
The process of building a Bayesian network model is often a bottleneck in applying the Bayesian netw...
When building a Bayesian belief network, usually a large number of probabilities have to be assessed...
The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic...
AbstractEmpirical evidence shows that naive Bayesian classifiers perform quite well compared to more...
The sensitivities revealed by a sensitivity anal-ysis of a probabilistic network typically depend on...
The paper discusses the problem of sensitivity analysis in normal Bayesian networks. The algebraic s...
When using a computer model to inform a decision, it is important to investigate any uncertainty in ...
The assessments for the various conditional probabilities of a Bayesian belief network inevitably ar...
A probabilistic network built for an application domain often has a single output variable of intere...
A probabilistic network built for an application domain often has a single output variable of intere...
Several recent works show that sensitivity analysis (SA) of decision-support models shares distincti...
Several recent works show that sensitivity analysis (SA) of decision-support models shares distincti...