Data Analysis with Bayesian Models

In this paper, we propose to review some Bayesian data analysis models, namely the models with one and several parameters. Using statistical data and expert estimates, we develop the methodology for probabilistic models construction in the form of Bayesian networks. The methodology enables us to construct high adequacy probabilistic models for solving classification and forecasting problems. Furthermore, we propose an integrated dynamic network model by combining probabilistic and regression approaches. This model stands out against the known ones for its ability to estimate multistep forecasts. The forecast estimates computed by applying the dynamic model are compared with the results obtained by employing logistic and multiple regressions. The best results were obtained in this case with the combined dynamic net model.

Publication year: 
2012
Issue: 
1
УДК: 
519-866
С. 40—54. Іл. 1. Табл. 1. Бібліогр.: 23 назви.
References: 

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