Add to ORKGVariables are rarely, if ever, measured without error. In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measurement errors. A mixed measurement error model is introduced to model the respondent error. Then an Object-Oriented Bayesian network, implementing the model above, is developed to represent how the actually observed values are generated from the original ones. Furthermore, potentialities and possible extensions of such an approach are discussed
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
Measurement error is the difference between the value provided by the respondent and the true (but u...
Structure learning algorithms that learn the graph of a Bayesian network from observational data oft...
Variables are rarely, if ever, measured without error. In this paper we propose to use the Object-O...
Variables are rarely, if ever, measured without error. In this paper we propose to use the Object-Or...
In this paper Object-Oriented Bayesian networks are proposed as a tool to model measurement errors ...
In this article, Object-Oriented Bayesian Networks (OOBN) are proposed as a tool to model measureme...
In this paper Object-Oriented Bayesian networks are proposed as a tool to model measurement errors ...
Measurement error is the difference between the value of a characteristic provided by the respondent...
Measurement error is the difference between a feature value provided by the respondent and the corre...
In this paper we propose to use the object-oriented Bayesian network architecture to model measureme...
In this paper we propose to use the object-oriented Bayesian networks (OOBNs) architecture to model...
In this paper the quality of data produced by national statistical institutes and by governmental i...
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
Measurement error is the difference between the value provided by the respondent and the true (but u...
Structure learning algorithms that learn the graph of a Bayesian network from observational data oft...
Variables are rarely, if ever, measured without error. In this paper we propose to use the Object-O...
Variables are rarely, if ever, measured without error. In this paper we propose to use the Object-Or...
In this paper Object-Oriented Bayesian networks are proposed as a tool to model measurement errors ...
In this article, Object-Oriented Bayesian Networks (OOBN) are proposed as a tool to model measureme...
In this paper Object-Oriented Bayesian networks are proposed as a tool to model measurement errors ...
Measurement error is the difference between the value of a characteristic provided by the respondent...
Measurement error is the difference between a feature value provided by the respondent and the corre...
In this paper we propose to use the object-oriented Bayesian network architecture to model measureme...
In this paper we propose to use the object-oriented Bayesian networks (OOBNs) architecture to model...
In this paper the quality of data produced by national statistical institutes and by governmental i...
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measure...
Measurement error is the difference between the value provided by the respondent and the true (but u...
Structure learning algorithms that learn the graph of a Bayesian network from observational data oft...