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Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for… (More)

Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the forecast and on the event or value that materializes. A scoring rule is strictly proper if the forecaster maximizes the expected score for an observation drawn from the distribution F if she issues the probabilistic forecast F , rather than any G 6= F .… (More)

Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model… (More)

- Chris Fraley, Adrian E. Raftery
- Comput. J.
- 1998

We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian components with… (More)

The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of Friedman and Rubin (1967). However, as currently implemented, it does not allow the specification of which features (orientation, size and shape) are to be… (More)

Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a speci ed relation between actors. We develop a class of models where the… (More)

- Ka Yee Yeung, Chris Fraley, A. Murua, Adrian E. Raftery, Walter L. Ruzzo
- Bioinformatics
- 2001

MOTIVATION
Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated… (More)

We introduce the weighted likelihood bootstrap (WLB) as a simple way of approximately simulating from a posterior distribution. This is easy to implement, requiring only an algorithm for calculating the maximum likelihood estimator, such as the EM algorithm or iteratively reweighted least squares; it does not necessarily require actual calculation of the… (More)

- David Madigan, Adrian E. Raftery, +4 authors Nanny Wermuth
- 1993

We consider the problem of model selection and accounting for model uncertainty in high dimensional contingency tables motivated by expert system applications The approach most used currently is a stepwise strategy guided by tests based on approxi mate asymptotic P values leading to the selection of a single model inference is then conditional on the… (More)

We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem involves averaging over all possible models (i.e.,… (More)