Adrian E. Raftery

Adrian is Blumstein-Jordan Professor of Statistics and Sociology and a faculty affiliate of the Center for Statistics and the Social Sciences and the Center for Studies in Demography and Ecology at the University of Washington. He works on the development of new statistical methods for the social, environmental and health sciences.

• Working Group on Applied, Bayesian and Computational Statistics, Winter 2010

News

• The iterativeBMA package in Bioconductor has 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 January 22, 2010