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Bayesian identifiability and misclassification in multinomial data
Author(s) -
Swartz Tim B.,
Haitovsky Yoel,
Vexler Albert,
Yang Tae Y.
Publication year - 2004
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315930
Subject(s) - identifiability , multinomial distribution , dirichlet distribution , gibbs sampling , bayesian probability , computer science , prior information , type (biology) , sampling (signal processing) , mathematics , statistics , econometrics , algorithm , artificial intelligence , filter (signal processing) , computer vision , mathematical analysis , ecology , biology , boundary value problem
The authors consider the Bayesian analysis of multinomial data in the presence of misclassification. Misclassification of the multinomial cell entries leads to problems of identifiability which are categorized into two types. The first type, referred to as the permutation‐type nonidentifiabilities, may be handled with constraints that are suggested by the structure of the problem. Problems of identifiability of the second type are addressed with informative prior information via Dirichlet distributions. Computations are carried out using a Gibbs sampling algorithm.