Abstract
The assessment of some characteristic, like rate or intensity, of episodic behavior is needed in various socially–oriented areas. Estimates of this characteristic may be used for risk analysis. But sometimes self–reports are the only feasible way to acquire the data on episodes of the behavior, and this approach often leads to imprecise and incomplete data that needs to be handled with an appropriate mathematical model. The paper focuses on the Bayesian belief network approach for estimation of intensity of episodic behavior with limited data on episodes in the framework of the gamma Poisson model of behavior. For the vine–copula specification of the network’s structure the copula types are identified and their parameters estimated with the synthetic data. These results are needed for the behavior modeling in data sparse areas, as the copula–vine approach reduces the computational burden on data while maintaining the dependencies of the gamma Poisson model.
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The work is published under the financial support of technical task SPC RAS No FFZF-2022-0003 and RFBR No 20-07-00839.
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Stoliarova, V., Tulupyev, A. (2023). Assessment of the Vine–Copula for the Gamma Poisson Model of Risky Person's Behavior with Synthetic Data. In: Dolinina, O., et al. Artificial Intelligence in Models, Methods and Applications. AIES 2022. Studies in Systems, Decision and Control, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-22938-1_7
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DOI: https://doi.org/10.1007/978-3-031-22938-1_7
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