Mathematical Sciences: Asymptotic Methods for Bayesian and  Likelihood Analysis
{'Code': '03040107', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alan Izenman', 'PO_EMAI': None, 'PO_PHON': None}
The primary goal of this research is to provide widely                  applicable methodology for Bayesian analysis based on accurate,                 higher-order asymptotic theory.  The main analytical tools to be                refined and employed are based on Laplace's method for                          approximating integrals and the theory of weak convergence of                   distributions.  The effect of the parameterization on the size of               apporximation errors will be explored and diagnostic tools for                  assessing the adequacy of an approximation will be obtained.                    Approximations for problems with multimodal posterior densities                 and margianl posterior densities of implicitly defined functions                will be dervied.  The first order behavior of posterior                         distributions based on general nonparametric prior distributions                on densities and hazard functions will be investigated and the                  possibility of extending second order approximations to                         nonparametric, infinite dimensional problems will be explored.                          Bayesian methods are aimed at improving our ability to                  incorporate existing (prior) information into decision and                      prediction strategies.  The Baysian approach has remained rather                philosophical until the recent computer revolution.  The current                thrust of excitement and activity in this area derives from the                 recently obtained computational capability brought by the                       computer revolution and the critical need brought by inundation                 of information relevant to any subject of interest.  These                      scientists are leaders in the effort to dramatically improve                    computational capabilities for Bayesian methods.  Their research                will create more powerful statistical techniques for                            comprehensively using informaion in the decision making process.
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{'Name': 'University of Minnesota-Twin Cities', 'CityName': 'MINNEAPOLIS', 'ZipCode': '554552009', 'PhoneNumber': '6126245599', 'StreetAddress': '200 OAK ST SE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Minnesota', 'StateCode': 'MN', 'CONGRESSDISTRICT': '05', 'CONGRESS_DISTRICT_ORG': 'MN05', 'ORG_UEI_NUM': 'KABJZBBJ4B54', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF MINNESOTA', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1269', 'Text': 'STATISTICS'}, {'Code': '1271', 'Text': 'COMPUTATIONAL MATHEMATICS'}]
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