Robert L. Wolpert
Professor Emeritus of Statistical Science
I'm a stochastic modeler-- I build computer-resident mathematical models
for complex systems, and invent and program numerical algorithms for making
inference from the models. Usually this involves predicting things that
haven't been measured (yet). Always it involves managing uncertainty and
making good decisions when some of the information we'd need to be fully
comfortable in our decision-making is unknown.
Originally trained as a mathematician specializing in probability theory and
stochastic processes, I was drawn to statistics by the interplay between
theoretical and applied research- with new applications suggesting what
statistical areas need theoretical development, and advances in theory and
methodology suggesting what applications were becoming practical and so
interesting. Through all of my statistical interests (theoretical, applied,
and methodological) runs the unifying theme of the <STRONG>Likelihood
Principle</STRONG>, a constant aid in the search for sensible methods of
inference in complex statistical problems where commonly-used methods seem
unsuitable. Three specific examples of such areas are:
* Computer modeling, the construction and analysis of fast small Bayesian
statistical emulators for big slow simulation models;
* Meta-analysis, of how we can synthesize evidence of different sorts
about a statistical problem; and
* Nonparametric Bayesian analysis, for applications in which common
parametric families of distributions seem unsuitable.
Many of the methods in common use in each of these areas are hard or
impossible to justify, and can lead to very odd inferences that seem to
misrepresent the statistical evidence. Many of the newer approaches
abandon the ``iid'' paradigm in order to reflect patterns of regional
variation, and abandon familiar (e.g. Gaussian) distributions in order to
reflect the heavier tails observed in realistic data, and nearly all of
them depend on recent advances in the power of computer hardware and
algorithms, leading to three other areas of interest:
* Spatial Statistics,
* Statistical Extremes, and
* Statistical computation.
I have a special interest in developing statistical methods for application
to problems in Environmental Science, where traditional methods often fail.
Recent examples include developing new and better ways to estimate the
mortality to birds and bats from encounters with wind turbines; the
development of nonexchangeable hierarchical Bayesian models for
synthesizing evidence about the health effects of environmental pollutants;
and the use of high-dimensional Bayesian models to reflect uncertainty in
mechanistic environmental simulation models. <P> My current (2015-2016)
research involves modelling and Bayesian inference of dependent time series
and (continuous-time) stochastic processes with jumps (examples include
work loads on networks of digital devices; peak heights in mass
spectrometry experiments; or multiple pollutant levels at spatially and
temporally distributed sites), problems arising in astrophysics (Gamma ray
bursts) and high-energy physics (heavy ion collisions), and the statistical
modelling of risk from, e.g., volcanic eruption.
Collaborative Research: Using Precursor Information to Update Probabilistic Hazard Maps awarded by National Science Foundation (Principal Investigator). 2018 to 2020
Collaborative Research: SI2-SSI: Jet Energy-loss Tomography with a Statistically and Computationally Advanced Program Envelope (JETSCAPE) awarded by National Science Foundation (Co-Principal Investigator). 2016 to 2020
Hazards SEES: Persistent volcanic crises -- resilience in the face of prolonged and uncertain risk awarded by State University of New York - Buffalo (Principal Investigator). 2015 to 2019
Collaborative Research: Advances Statistical Surrogates for Linking Multiple Computer Models with Disparate Data for Quantifying Uncertain Hazards awarded by National Science Foundation (Principal Investigator). 2016 to 2019
EMSW21-RTG: Geometric, Topological awarded by National Science Foundation (Key Faculty). 2011 to 2018
Collaborative Research: Statistical And Computational Models and Methods for Extracting Knowledge from Massive Disparate awarded by National Science Foundation (Principal Investigator). 2012 to 2015
Hazards SEES Type 1: Persistent volcanic crises in the USA: from precursors to resilience awarded by University of Hawaii System (Principal Investigator). 2013 to 2015
FRG: Collaborative Research: Prediction and Risk of Extreme Events Utilizing Mathematical Computer Models of Geophysical awarded by National Science Foundation (Principal Investigator). 2008 to 2012
SCREMS: Distributed Environments for Stochastic Computation awarded by National Science Foundation (Co-Principal Investigator). 2004 to 2007
Spatial-temporal Models for Environmental Health Effects awarded by Environmental Protection Agency (Co-Principal Investigator). 2001 to 2006
Clyde, MA, and Wolpert, RL. "Discussion of ``Polson and Scott: Shrink globally, act locally: Sparse Bayesian regularization and prediction''." Bayesian Statistics 9. Ed. Bernardo, JM, Bayarri, MJ, Berger, JO, Dawid, AP, Heckerman, D, Smith, AFM and West, M.: Oxford University Press. 2011. 528-529.
Clyde, MA, and Wolpert, RL. "Nonparametric Function Estimation using Overcomplete Dictionaries (with Discussion)." Bayesian Statistics 8. Ed. Bernardo, JM, Bayarri, MJ, Berger, JO, Dawid, AP, Heckerman, D, Smith, AFM and West, M.: Oxford University Press. 2007. 91-114.
Clyde, MA, House,, L, and Wolpert, RL. "Nonparametric Models for Proteomic Peak Identification and Quantification." Bayesian Inference for Gene Expression and Proteomics. Ed. Do, K-A, Müller, P and Vannucci, M.: Cambridge University Press. 2006. 293-308. (Chapter)
Clyde, MA, House, LL, Wolpert, RL, and Vannucci, M. "Nonparametric Models for Proteomic Peak Identification and Quantification." Bayesian Inference for Gene Expression and Proteomics.: Cambridge University Press.. 293-308. Full Text
Wolpert, RL, Spiller, ET, and Calder, ES. "Dynamic statistical models for pyroclastic density current generation at soufrière hills volcano." Frontiers in Earth Science 6 (May 23, 2018). Full Text
Benjamin, DJ, Berger, JO, Johannesson, M, Nosek, BA, Wagenmakers, EJ, Berk, R, Bollen, KA, Brembs, B, Brown, L, Camerer, C, Cesarini, D, Chambers, CD, Clyde, M, Cook, TD, De Boeck, P, Dienes, Z, Dreber, A, Easwaran, K, Efferson, C, Fehr, E, Fidler, F, Field, AP, Forster, M, George, EI, Gonzalez, R, Goodman, S, Green, E, Green, DP, Greenwald, AG, Hadfield, JD, Hedges, LV, Held, L, Hua Ho, T, Hoijtink, H, Hruschka, DJ, Imai, K, Imbens, G, Ioannidis, JPA, Jeon, M, Jones, JH, Kirchler, M, Laibson, D, List, J, Little, R, Lupia, A, Machery, E, Maxwell, SE, McCarthy, M, Moore, DA, Morgan, SL, Munafó, M, Nakagawa, S, Nyhan, B, Parker, TH, Pericchi, L, Perugini, M, Rouder, J, Rousseau, J, Savalei, V, Schönbrodt, FD, Sellke, T, Sinclair, B, Tingley, D, Van Zandt, T, Vazire, S, Watts, DJ, Winship, C, Wolpert, RL, Xie, Y, Young, C, Zinman, J, and Johnson, VE. "Redefine statistical significance." Nature Human Behaviour 2.1 (January 1, 2018): 6-10. Full Text Open Access Copy
Kyzyurova, KN, Berger, JO, and Wolpert, RL. "Coupling computer models through linking their statistical emulators." Siam Asa Journal on Uncertainty Quantification 6.3 (January 1, 2018): 1151-1171. Full Text
Cao, S, Park, C, Barbieri, RA, Bass, SA, Bazow, D, Bernhard, J, Coleman, J, Fries, R, Gale, C, He, Y, Heinz, U, Jacak, BV, Jacobs, PM, Jeon, S, Kordell, M, Kumar, A, Luo, T, Majumder, A, Nejahi, Y, Pablos, D, Pang, LG, Putschke, JH, Roland, G, Rose, S, Schenke, B, Schwiebert, L, Shen, C, Sirimanna, C, Soltz, RA, Velicanu, D, Vujanovic, G, Wang, XN, and Wolpert, RL. "Multistage Monte Carlo simulation of jet modification in a static medium." Physical Review C 96.2 (August 22, 2017). Full Text
Ernst, PA, Brown, LD, Shepp, L, and Wolpert, RL. "Stationary Gaussian Markov processes as limits of stationary autoregressive time series." Journal of Multivariate Analysis 155 (March 2017): 180-186. Full Text
Lunagómez, S, Mukherjee, S, Wolpert, RL, and Airoldi, EM. "Geometric Representations of Random Hypergraphs." Journal of the American Statistical Association 112.517 (January 2, 2017): 363-383. Full Text
Bernhard, JE, Marcy, PW, Coleman-Smith, CE, Huzurbazar, S, Wolpert, RL, and Bass, SA. "Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison." Physical Review C Nuclear Physics 91.5 (May 22, 2015). Full Text
Bayarri, MJ, Berger, JO, Calder, ES, Patra, AK, Pitman, EB, Spiller, ET, and Wolpert, RL. "PROBABILISTIC QUANTIFICATION OF HAZARDS: A METHODOLOGY USING SMALL ENSEMBLES OF PHYSICS-BASED SIMULATIONS AND STATISTICAL SURROGATES." International Journal for Uncertainty Quantification 5.4 (2015): 297-325. Full Text
Mahmood, A, Wolpert, RL, and Pitman, EB. "A physics-based emulator for the simulation of geophysical mass flows." Siam Asa Journal on Uncertainty Quantification 3.1 (January 1, 2015): 562-585. Full Text
Wolpert, RL, Ickstadt, K, and Hansen, MB. "A nonparametric Bayesian approach to inverse problems." 2003.
Ickstadt, K, and Wolpert, RL. "Spatial regression for marked point processes." 1999.
Wolpert, RL, and Lavine, M. "Markov random field priors for univariate density estimation." 1996. Full Text
WARRENHICKS, WJ, and WOLPERT, RL. "PREDICTIVE MODELS OF FISH RESPONSE TO ACIDIFICATION - USING BAYESIAN-INFERENCE TO COMBINE LABORATORY AND FIELD-MEASUREMENTS." 1994.