Professor, Department of Statistics
Mark Berliner was born in Chicago, Illinois, in 1951 and was raised in the Chicago area. He received a B.S. in Mathematics with a Minor in Physics in 1974, followed by an M.S. in Statistics in 1976, both from Purdue University. In 1980 he received a Ph.D. in Statistics, again at Purdue; his thesis advisor was Professor James Berger.
He joined The Ohio State University Department of Statistics as Assistant Professor in 1980. He was Visiting Assistant Professor of Statistics at the University of Michigan in 1984, and became Associate Professor of Statistics at Ohio State in 1986 and Professor in 1994.
His early research focused on Bayesian statistics, decision theory, and robust Bayesian analysis. He was also Biostatistician, Ohio State Comprehensive Cancer Center during 1987-88. In the late 1980s, he began research on the statistics of chaos and dynamical systems. Berliner served as Geophysical Statistics Project Leader, 1995-1997, at the National Center for Atmospheric Research in Boulder, Colorado. He was appointed Senior Fellow and Project Manager for Numerical Modeling at the National Institute of Statistical Sciences, Research Triangle Park, North Carolina, 1997-1999. His current research area is environmental statistics, focusing on Bayesian analyses in the weather and climate sciences.
His research activities have been supported by the Office of Naval Research, the Exxon Educational Foundation, the National Science Foundation, the National Aeronautics and Space Administration, and the U.S. Environmental Protection Agency. He has served as Associate Editor, Journal of the American Statistical Association: Theory and Methods; Publications Officer, Section on Bayesian Statistical Science, American Statistical Association; and is currently an Elected Member of the International Board of Advisors of the International Society for Bayesian Analysis. He is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science.
His general interest is the implementation of Bayesian analysis in complex settings, with particular attention to geophysical problems. The Bayesian paradigm provides opportunities for the combination of physical reasoning and observational data in a coherent analysis framework, but in a fashion that manages the uncertainties in both information sources. A key to the modeling is the hierarchical viewpoint in which separate statistical models are developed for the physical variables studied and the observations conditional on those variables. Modeling physical variables in this way enables incorporation of scientific models across a spectrum of levels of intensity ranging from qualitative use of physical reasoning to strong reliance on numerical models. Modeling and computational methods are being developed and applied to problems of assessing climate change and its impacts, weather forecasting, glacial dynamics, and medium-range climate prediction.