A framework for uncertainty quantification and prior knowledge integration.
Bayesian statistics provides a mathematical data analysis framework for representing uncertainty and incorporating prior knowledge into statistical inference. In Bayesian statistics, probabilities are used to represent the uncertainty in parameters rather than the data itself. This approach allows for the incorporation of prior information and the use of subjective and objective beliefs about the parameters.
Faculty studying bayesian analysis.
James Booth
Professor of Statistics and Data Science, Department Chair
James Booth
Professor of Statistics and Data Science, Department Chair
Contact
jim.booth@cornell.edu
Dan Kowal
Associate Professor of Statistics and Data Science
Dan Kowal
Associate Professor of Statistics and Data Science
Contact
dan.kowal@cornell.edu
David S. Matteson
Professor, Statistics and Data Science, Director of the National Institute of Statistical Sciences
David S. Matteson
Professor, Statistics and Data Science, Director of the National Institute of Statistical Sciences
Contact
Matteson <at> cornell <dot> edu
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
David Ruppert
Andrew Schultz Jr. Professor of Engineering, School of Operations Research and Information Engineering, Professor of Statistics and Data Science
Contact
dr24@cornell.edu
Martin T. Wells
Charles A. Alexander Professor of Statistical Sciences, Director of Undergraduate Studies, Statistics and Data Science
Martin T. Wells
Charles A. Alexander Professor of Statistical Sciences, Director of Undergraduate Studies, Statistics and Data Science
Contact
mtw1@cornell.edu
