- Autor
- Grzenda Wioletta (Warsaw School of Economics, Poland)
- Tytuł
- Informative Versus Non-Informative Prior Distributions and Their Impact on the Accuracy of Bayesian Inference
- Źródło
- Statistics in Transition, 2016, vol. 17, nr 4, s. 763-780, tab., wykr., bibliogr. s. 780
- Słowa kluczowe
- Bezrobocie, Modele regresji, Wnioskowanie bayesowskie, Metoda Monte Carlo
Unemployment, Regression models, Bayesian inference, Monte Carlo method - Uwagi
- summ.
Materiały z konferencji Multivariate Statistical Analysis 2015, Łódź - Abstrakt
- In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and noninformative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling. (original abstract)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach - Pełny tekst
- Pokaż
- Bibliografia
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- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng