by Jukka Corander
Research Report 1997:12
Department of Statistics, Stockholm University, S-106 91 Stockholm, Sweden
Subjective classifications of objects where the categories are not specified in advance are considered. Several classifications of the same object set are assumed to be available. A probability model is introduced to capture consensus and variability between the classifications. In its simplest form the model distributes the classifications in accordance with a unimodal distribution. A generalization based on mixture distributions is used to describe partial consensus among the calssifications. Inference concerning consensus classifications and variability parameters is made possible by applying simulation algorithms and a modified likelihood approach.
Key words: Consensus of Classifications, Markov Chain Monte Carlo Methods, Random Graphs, Subjective Classifications.
Close this Window
Last update: 1997-12-16 / KH