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Bayesian Model Selection in Social Research
It is argued that P-values and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independentExpand
Strictly Proper Scoring Rules, Prediction, and Estimation
Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is properExpand
Model-Based Clustering, Discriminant Analysis, and Density Estimation
TLDR
This work reviews a general methodology for model-based clustering that provides a principled statistical approach to important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. Expand
Model-based Gaussian and non-Gaussian clustering
Abstract : 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 theExpand
Bayesian Model Averaging: A Tutorial
TLDR
Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty and provides improved out-of- sample predictive performance. Expand
Latent Space Approaches to Social Network Analysis
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 nodesExpand
Using Bayesian Model Averaging to Calibrate Forecast Ensembles
Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessingExpand
Probabilistic forecasts, calibration and sharpness
Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation ofExpand
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
TLDR
The problems of determining the number of clusters and the clustering method are solved simultaneously by choosing the best model, and the EM result provides a measure of uncertainty about the associated classification of each data point. Expand
Bayesian Model Averaging for Linear Regression Models
Abstract 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 theExpand
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