Adrian E. Raftery

Adrian is Blumstein-Jordan Professor of Statistics and Sociology and Director of the Center for Statistics and the Social Sciences at the University of Washington. He is a former Coordinating Editor and Applications and Case Studies Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He works on the development of new statistical methods for the social, environmental and health sciences.

• Working Group on Model-Based Clustering, Summer 2008 Schedule

News

• The iterativeBMA package in Bioconductor has just been released. This is a variable selection and classification algorithm with an application to classifying 2-class microarray samples, as described by Yeung, Bumgarner and Raftery (Bioinformatics 2005).

• The website got a big update on May 22, 2008, when recent publications and technical reports and working papers were added. The Research pages were also updated.

• Recent papers on probabilistic weather forecasting:
  • Probabilistic Quantitative Precipitation Forecasting using a Two-Stage Spatial Model.
  • ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging.
  • Probabilistic forecasts, calibration and sharpness.
  • Strictly Proper Scoring Rules, Prediction, and Estimation. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts.
  • Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging (with Discussion).
  • Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging.

• Recent papers on model-based clustering and social networks:
  • Representing Degree Distributions, Clustering, and Homophily in Social Networks with Latent Cluster Random Effects Models.
  • MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering.
  • Model-based clustering for social networks (with Discussion).
  • Model-based Clustering with Dissimilarities: A Bayesian Approach.
  • Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering.
  • Model-based methods of classification: Using the mclust software in chemometrics.

• Recent papers on Bayesian model averaging:
  • Online Prediction Under Model Uncertainty Via Dynamic Model Averaging:Application to a Cold Rolling Mill.
  • Determining Growth Determinants: Default Priors and Predictive Performance in Bayesian Model Averaging.
  • Bayesian Robust Variable and Transformation Selection: A Unified Approach.
  • Markov chain Monte Carlo with mixtures of singular distributions.
  • Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity (with Discussion).

• Recent papers on Bayesian melding for deterministic models:
  • Bayesian melding for estimating uncertainty in national HIV prevalence estimates.
  • Probabilistic projections of HIV prevalence using Bayesian melding.
  • Assessing Uncertainty in Urban Simulations Using Bayesian Melding.

  Last updated July 11, 2008