New probabilistic versions of the SIMCA and CLASSY classification methods. Part 1 : Theoretical description
article
The probabilistic SIMCA and CLASSY methods for multivariate classification are defined and explained in detail. The differences between the present algorithms and previous versions are described. Both probabilistic SIMCA and CLASSY methods construct principal-component class models and assume an ormal distribution for the residuals. The methods differ in the distributional assumptions for the object scores within the class model space. Details are given for the construction of probability density functions which conform to the model assumptions, and which can be substituted in Bayes' theorem to obtain posterior classification probabilities. © 1987
TNO Identifier
825475
ISSN
0003-2670
Source
Analytica Chimica Acta, 192, pp. 63-75.
Pages
63-75
Files
To receive the publication files, please send an e-mail request to TNO Repository.