@misc{Zmyślona_Beata_Zastosowanie_2005,
 author={Zmyślona, Beata},
 year={2005},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Wydawnictwo Akademii Ekonomicznej im. Oskara Langego we Wrocławiu},
 description={Prace Naukowe Akademii Ekonomicznej we Wrocławiu; 2005; nr 1097, s. 147-158},
 language={pol},
 abstract={The study of associations among categorical features requires using some techniques, for example a loglinear model. The Bayesian iterative proportional fitting algorithm (Bayesian IPF) is the simulation Monte Carlo technique of estimation of loglinear model parameters in case of incomplete data sets. In this technique we create pseudorandom draws from the posterior distribution of parameters and from the conditional distribution of missing values. The main aim of using this approach is to improve statistical inference through elimination of estimator bias and to correct estimation of standard errors. In this paper we present the theoretical background of Bayesian IPF and its application to impute missing data through generating values from conditional distribution.},
 type={artykuł},
 title={Zastosowanie bayesowskiej metody iteracyjnego dopasowania proporcjonalnego do uzupełniania brakujących danych},
}