Mitchell Prize

The Mitchell Prize is awarded in recognition of an outstanding paper that describes how a Bayesian analysis has solved an important applied problem. The Prize is jointly sponsored by the Section on Bayesian Statistical Science (SBSS) of the ASA, the International Society for Bayesian Analysis (ISBA), and the Mitchell Prize Founders’ Committee.  The fund was initiated by donations from Toby’s many friends and colleagues at the Oak Ridge National Laboratories, Duke University, Peter Rossi, Jerome Sacks, Microsoft Research, the Section on Bayesian Statistical Science of the ASA, University of California at Los Angeles and Donald Ylvisaker.

Toby J. Mitchell

The Mitchell Prize is named after Toby J. Mitchell and was established by his friends and colleagues following his death from leukemia in 1993.

Toby was a Senior Research Staff Member at Oak Ridge National Laboratory throughout his career, with leaves of absence spent at the University of Wisconsin and at the National Institute of Environmental Health Sciences. Toby won the Snedecor Award in 1978 (with co-author Bruce Turnbull), made incisive contributions to statistics, especially in biometry and engineering applications, and was a marvelous collaborator and an especially thoughtful scientist. Toby was a dedicated Bayesian, hence the focus of the prize.

Eligibility and Application Procedure

To be eligible for the Mitchell Prize a paper must have been published or accepted for publication in a refereed journal or conference proceedings during the two years preceeding the nomination. A paper may be nominated by an author or any member of ISBA or SBSS. A complete nomination consists of:

  • An electronic file of the paper being nominated, in .pdf format.
  • A letter of nomination (also in .pdf format) describing the work’s eligibility for the Mitchell Prize, that is, why it is “an outstanding paper that describes how a Bayesian analysis has solved an important applied problem.”
  • The names of two evaluators (not the nominator or coauthors) who are willing and able to evaluate credibly the usefulness of the work from the perspective of the applied field addressed in the paper, as distinct from providing comments on its statistical merit.
  • Contact information for nominee, nominator (if different) and evaluators.

The Prize includes a commemorative plaque and an award of $1,000. Nominated papers will be evaluated by the Mitchell Award Committee, appointed by the ISBA Prize Committee.

Winners of the Mitchell Prize

2017
Daniele Durante and David B. Dunson for their paper “Bayesian Inference and Testing of Group Differences in Brain Networks,” published in Bayesian Analysis, 13(1), 29-58, 2018.

2016
Lin Lin, Greg Finak, Kevin Ushey, Chetan Seshadri, Thomas R Hawn, Nicole Frahm, Thomas J Scriba, Hassan Mahomed, Willem Hanekom, Pierre-Alexandre Bart, Giuseppe Pantaleo, Georgia D Tomaras, Supachai Rerks-Ngarm, Jaranit Kaewkungwal, Sorachai Nitayaphan, Punnee Pitisuttithum, Nelson L Michael, Jerome H Kim, Merlin L Robb, Robert J O’Connell, Nicos Karasavvas, Peter Gilbert, Stephen C De Rosa, M Juliana McElrath, and Raphael Gottardo for their paper “COMPASS Identifies T-cell Subsets Correlated with Clinical Outcomes,” published in the Nature Biotechnology, 33(6), 610–616, 2015.

2015
Yanxun Xu, Peter Müller, Yuan Yuan, Kamalakar Gulukota, and Yuan Ji for their paper “MAD Bayes with Tumor Heterogeneity–Feature Allocation with Exponential Family Sampling,” published in the Journal of the American Statistical Association, 110, 503-514, 2015.

2014
Nimar S. Arora, Stuart Russell, and Erik SudderthNET-VISA: Network Processing Vertically Integrated Seismic Analysis. (Bulletin of the Seismological Society of America 103: 709-729)

2013
Donatello Telesca, Elena Erosheva, Derek Kreager, and Ross Matsueda. Modeling Criminal Careers as Departures from a Unimodal Population Age-Crime Curve: the Case of Marijuana Use (JASA 107: 1427-1440).

2012
Ioanna Manolopoulou,Melanie Matheu,Michael CahalanMike West, and Thomas Kepler. Bayesian Spatio-Dynamic Modelling in Cell Motility Studies: Learning Nonlinear Taxic Fields Guiding the Immune Response (JASA 107:855–865 (with disc. pp 865 –874)).

2011
Philippe Lemey, Andrew Rambaut, Alexei J. Drummond, and Marc A. Suchard. Bayesian Phylogeography Finds Its Roots (PLoS Comput Biol 5(9)).

2010
Ian Vernon, Michael Goldstein and Richard G. Bower. Galaxy Formation: A Bayesian Uncertainty Analysis (Bayesian Analysis 5: 619-670).

2009
Ricardo Lemos and Bruno Sanso. A Spatio-Temporal Model for Mean, Anomaly and Trend Fields of North Atlantic Sea Surface Temperature (JASA 104: 5-18).

2008
Hui Jin and Donald Rubin.  Principal Stratification for Causal Inference With Extended Partial Compliance (JASA 103: 101-111).

2007
Tian Zheng, Matthew J. Salganik, and Andrew German. How Many People Do You Know in Prison? Using Overdispersion in Count Data to Estimate Social Structure in Networks (JASA 101: 409-423).

2006
Ben Redelings and Marc Suchard.  Joint Bayesian Estimation of Alignment and Phylogeny (Systematic Biology 54: 401-418).

2003
Jeff Morris, Marina Vannucci, Phil Brown and Ray Carroll.  Wavelet-Based Nonparametric Modeling of Hierarchical Function in Colon Carcinogenesis (with discussion) (JASA 98: 573-597).

2002
Jonathan K. Pritchard, Matthew Stephens & Peter Donnelly.  Inference of Population Structure Using Multilocus Genotype Data (Genetics 155: 945-959).

2001
Keisuke Hirano, Guido Imbens, Donald Rubin and Xiao-Hua Zhou.  Assessing the Effect of an Influenza Vaccine in an Encouragement Design (Biostatistics 1: 69-88).

2000
Jun Liu, Andrew Neuwald and Chip Lawrence.  Markovian Structures in Biological Sequence Alignments (JASA 94: 1-15).

1999
Alan Montgomery and Peter Rossi.  Estimating Price Elasticities with Theory-Based Priors (J Marketing Research 36(4): 413-423).

1997
Mike West.  Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models. Published as Hierarchical Mixture Models in Neurological Transmission Analysis (JASA 92: 587-606).

1994
Mike West.  Some Statistical Issues in Palaeoclimatology (with discussion). In Bayesian Statistics 5 (eds: J Berger et al.), Oxford University Press.