The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic structure of the conditional means and variances, as rational functions involving linear and quadratic functions of the parameters, are used to simplify the sensitivity analysis. In particular the probabilities of conditional variables exceeding given values and related probabilities are analyzed. Two examples of application are used to illustrate all the concepts and methods
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
A Bayesian network is a concise representation of a joint probability distribution, which can be use...
The paper discusses the problem of sensitivity analysis in normal Bayesian networks. The algebraic s...
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...
The assessments for the various conditional probabilities of a Bayesian belief network inevitably ar...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
We introduce a methodology for sensitivity analysis of evidence variables in Gaussian Bayesian netwo...
The process of building a Bayesian network model is often a bottleneck in applying the Bayesian netw...
The effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by su...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Summary. In many areas of science and technology, mathematical models are built to simu-late complex...
AbstractTo evaluate the impact of model inaccuracies over the network’s output, after the evidence p...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
A Bayesian network is a concise representation of a joint probability distribution, which can be use...
The paper discusses the problem of sensitivity analysis in normal Bayesian networks. The algebraic s...
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...
The assessments for the various conditional probabilities of a Bayesian belief network inevitably ar...
To study the effects of inaccuracies in the parameter probabilities of a Bayesian network, often a s...
We introduce a methodology for sensitivity analysis of evidence variables in Gaussian Bayesian netwo...
The process of building a Bayesian network model is often a bottleneck in applying the Bayesian netw...
The effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by su...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Summary. In many areas of science and technology, mathematical models are built to simu-late complex...
AbstractTo evaluate the impact of model inaccuracies over the network’s output, after the evidence p...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensiv...
A Bayesian network is a concise representation of a joint probability distribution, which can be use...