Bayesian classification based on multivariate binary data
β Scribed by W.O. Johnson; G.E. Kokolakis
- Publisher
- Elsevier Science
- Year
- 1994
- Tongue
- English
- Weight
- 922 KB
- Volume
- 41
- Category
- Article
- ISSN
- 0378-3758
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
This paper proposes a new data classi"cation method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are de"ned to be tolerant when their similarity measure e
## Abstract The authors describe a modelβbased kappa statistic for binary classifications which is interpretable in the same manner as Scott's pi and Cohen's kappa, yet does not suffer from the same flaws. They compare this statistic with the dataβdriven and populationβbased forms of Scott's pi in
## Answer justification refers to the ability of a computer program to explain how or why it arrived at a particular conclusion. This paper presents a new method for automated answer justification that is suitable for use in computer-supported decision aids in medicine which are based on Bayesian
This paper considers the use of a multivariate binomial probit model for the analysis of correlated exchangeable binary data. The model can naturally accommodate both cluster and individual level covariates, while keeping a fairly flexible intracluster association structure. We discuss Bayesian esti